0 引言

1 图像重建

1.1 基于深度学习的MRI图像重建

1.1.1 基于端到端深度学习的MRI重建方法

基于数据驱动的端到端深度学习MRI重建研究的重点在于利用深度学习网络学习欠采样数据到全采样数据(k空间或图像)的端到端映射关系。这类网络一般是在提取一组MRI数据的共同特征基础上，其特点是需要大量高质量的训练样本。目前常用于MRI重建的网络结构有U-Net(Zbontar等，2018；Wang等，2019)、生成对抗网络(generative adversarial network, GAN)(Yang等，2018a)、Cascade Net(Schlemper等，2018)、DeepcomplexMRI(Wang等，2020a)、ResNet(Cai等，2018)和AUTOMAP(Zhu等，2018a)等。在此基础上，各种改进的重建网络不断提出(Sun等，2019b；Xiang等，2019; Wu等，2018a；Ran等，2020；Zeng等，2020)。由于篇幅所限，现介绍两个典型的卷积神经网络(convolutional neural network, CNN)重建算法。

1) 基于级联CNN的MRI重建。Schlemper等人(2018)提出了一种对CNN进行级联而成的深层网络结构，并用于学习重建2维多帧动态心脏MRI。如图 1所示，该算法通过CNN模块对输入的降采样MR图像帧进行重建，通过数据一致性模块(data consistency，DC)在网络中融合图像在k空间的频域信息以辅助重建，两种模块以循环的方式进行级联，所以整个网络可以通过端到端的方式来重建。这样级联模式的优点在于网络的复杂度可以根据数据量的大小而调节。

图 1 基于深度级联卷积神经网络的动态MR图像重建流程图(Schlemper等，2018)

Fig. 1 Flowchart of deep cascade of convolutional neural networks for dynamic MR image reconstruction (Schlemper et al., 2018)

研究结果表明该算法的重建性能明显优于字典学习和压缩感知等传统方法，在重建误差与重建速度上都显著提升。同时该算法通过不同MR图像帧之间的参数共享，充分利用了动态图像序列中的空间相关性，因而在多帧MR图像序列的联合重建中也表现出了优异的性能。

2) 基于复数域CNN的MRI重建。MRI原理决定了扫描获得的原始数据是以复数形式构成。然而，目前基于深度学习的MRI重建方法一般仅利用幅值信息，或者是将复数MR图像的实部和虚部均视为两通道的实值数据，执行实数积，从而忽略了实部和虚部之间的相关性。充分利用复数的特性构建CNN网络可以提升MRI重建质量。

Wang等人(2020a)提出了一种基于复数卷积的残差网络(DeepcomplexMRI)用于并行MRI重建。该网络利用MR图像中实部和虚部之间的相关性，考虑k空间数据的拟合项，不需要额外地计算线圈灵敏度信息，可以自动学习跨通道的图像相关性。

图 2所示为DeepcomplexMRI网络的结构，网络由多个级联的块组成，每个块包括一个CNN单元和一个数据拟合单元。在网络的各层级联之间，获取的欠采样k空间数据用于更新层输出以增强数据的一致性。值得注意的是，CNN单元是一个复数的卷积网络，通过在每个CNN层中引入复数卷积来考虑相关性。

图 2 DeepcomplexMRI网络结构示意图(Wang等，2020a)

Fig. 2 Architecture of DeepcomplexMRI (Wang et al., 2020a)((a) network structure; (b) CNN unit)

1.1.2 基于先验模型的深度学习MRI重建方法

基于先验模型的深度学习重建方法是将传统的MRI重建模型与深度学习相结合，通过对优化算法进行网络化，从而学习重建模型的最优参数，而不是单纯地进行图像之间的映射(Sun等，2018, 2019a)，从而可以增加模型的泛化能力，减少对训练数据的需求。这类方法受到越来越多的关注，其中的典型算法包括交替方向乘子法(alternating direction method of multipliers，ADMM)(Yang等，2016)、基于模型的深度学习(model-based deep lerning, MoDL)(Aggarwal等，2019)、变分网络(variational network, VN-Net)(Hammernik等，2018)和卷积去混叠网络(convolutional de-aliasing network, ConvDe-AliasingNet)(Chen等，2019c；Zhang等，2020b；Liu等，2020)等。Liang等人(2020)分析指出各种基于模型的深度学习MRI重建方法与端到端深度学习重建的关联性。

本文列举较有代表性的两个基于模型的深度学习MRI重建算法。

1) 基于交替方向乘子法的MRI重建(ADMM-Net)。ADMM-Net是一个定义在数据流图上的深度架构(Yang等，2016, 2018a)。基于压缩感知MRI模型，图 3所示的流程图将成像模型的ADMM迭代过程展开为一种新型网络结构，成像模型和迭代算法的未知参数均转化为该网络的参数。在训练阶段，该算法使用L-BFGS(limited broyden-fletcher-goldfarb shanno)算法对网络中所有参数进行端到端训练。在测试阶段，所学习的ADMM-Net具有类似于ADMM算法的计算开销，但是使用从训练数据中学习到的参数进行基于压缩感知的核磁共振图像重建，即在k空间中以不同的欠采样率进行MRI图像重建。该方法是第一个将模型驱动的ADMM优化算法映射为可学习的深度网络架构的计算新框架。

图 3 ADMM-Net网络流程图(Yang等，2016)

Fig. 3 Flowchart of ADMM-Net (Yang et al., 2016)

2) 卷积去混叠网络用于并行MRI重建。Chen等人(2019c)提出了一种基于CNN模型优化的并行MRI重建算法(ConvDe-AliasingNet)，能充分利用并行MR图像的特点，即在空间域和线圈间具有冗余和相关的信息。该算法网络结构如图 4所示(图 4 (a)中，橙色箭头表示通过前向传播重建下采样k空间数据的过程，绿色箭头表示通过反向传播更新参数)，是对分裂伯格曼迭代算法进行网络化得到的。其中一次迭代拆分为图 4中蓝色标示的6层网络，网络输入为下采样的k空间数据。经过多次迭代，即可获得高质量的重建结果。图 4(b)(c)分别为一次迭代中的两个卷积执行的详细过程。

图 4 基于模型的去混叠网络用于pMRI重建的网络架构(Chen等，2019c)

Fig. 4 Convolutional de-aliasing network architecture for pMRI reconstruction (Chen et al., 2019c)

((a) the flow chart; (b) the detailed configurations of Conv1;(c) the detailed configurations of Conv2)

与经典的并行MRI方法如敏感度编码(sensitivity encoding, SENSE)、广义自动校准部分并行采集(generalized autocalibrating partially parallel acquisition, GRAPPA)以及并行图像重建(self-consistent parallel imaging reconstruction, SPIRiT)等相比，ConvDe-AliasingNet的加速倍数更高、重建速度更快(Chen等，2019c)。传统的并行重建算法，一般加速倍数为2~5×，而该算法在不影响医生诊断的条件下，加速倍数可达到9×，而且图像重建质量明显优于经典的并行MRI重建方法；与目前流行的端到端的深度学习方法相比，该算法在训练过程中不需要大数据量，且网络的稳定性比较好，泛化能力也比较强；同时与基于模型的深度学习并行MRI重建相比，该模型不需要计算线圈敏感度图。

1.2 基于深度学习的低剂量CT成像方法

1.2.1 基于编解码CNN的方法

1.2.2 结合小波变换的CNN的方法

小波变换具有多尺度的特点，能将图像信号分解为不同的频率分量，为基于CNN的图像去噪提供更丰富的信息，提升去噪性能。因此，结合小波变换的CNN也是进行LDCT去噪的一个常用方法。

Kang等人(2017a, 2018)提出了WavResNet算法，在2016年的LDCT重建挑战大赛中取得了第2名的优异成绩。如图 5所示，该算法采用轮廓波(contourlet)变换对原始的LDCT图像进行分解，得到不同方向、不同频率范围的15个子带通道；然后将这些子带通道送入CNN网络进行训练，得到15个信道的输出数据进行重构，最终输出重建后的图像。该类方法在网络的输入输出两段分别对图像进行小波变换和小波逆变换，而中间部分CNN则保持图像尺寸不变，在设计上与深度学习实现超分辨率重建的方法较为类似。

图 5 基于小波域去噪的深度卷积神经网络结构(Kang等，2017a)

Fig. 5 Deep convolutional neural network architecture for wavelet domain denoising (Kang et al., 2017a)

另一种结合小波变换与CNN的常用方式是在网络结构中嵌入小波变换。Han等人(2018)为解决U-Net生成的图像模糊且含有伪影的问题，用小波分解和重构代替原始CNN中的池化层和上采样层，实现了不同尺度上小波变换的运用；此外，额外的网络通道传输了不同子带的高频分量，可以帮助网络更好地恢复CT图像中的高频细节。此类方法考虑到扫描伪影是全局分布，所以将小波变换与多尺度CNN相结合；而编解码结构具备多尺度的特点，促使小波变换与编解码网络可以进行有效融合。

1.2.3 基于生成对抗网络的方法

1.2.4 基于正弦图域的深度学习方法

对于低剂量CT和稀疏角度CT，直接在传感器域对投影数据进行处理，然后再进行滤波反变换也是一种可行的方法。一些学者在这个方向上开展了初步研究，即使用常规的CNN对正弦图(sinogram)域的数据进行处理(Lee等，2018；Ghani和Karl，2018；Humphries等，2019；Yin等，2019)。其中，Yin等人(2019)提出了一种区域渐进3维残差网络(DP-ResNet)，如图 6所示，将整个成像流程分为3个阶段：正弦图域网络(sinogram domain sub-network, SD-Net)、滤波反投影(FBP)和图像域网络(image domain sub-network, ID-Net)。其中，SD-Net利用一个较浅的残差网络完成从低剂量投影数据到正常剂量投影数据的映射，而ID-Net则是对前一个网络的结果进行滤波反投影后，利用一个较深的残差网络完成最终去噪，两个网络在提高最终成像质量方面起到了互补的作用。

图 6 基于正弦图域的DP-ResNet算法流程(Yin等，2019)

Fig. 6 Flowchart of DP-ResNet based on sinogram domain(Yin et al., 2019)

由于原始数据难以获取，大部分LDCT研究还是围绕图像域展开, 这就导致基于正弦图域的方法较少，深度学习方法应用于正弦图域是否含有某些优势还有待进一步研究。

1.3 基于深度学习的超声成像方法

1.3.1 基于深度学习的传统B型超声成像方法

B型超声是临床应用最为广泛的超声成像。普通B型超声根据回波幅度重建图像，是临床实践中最常用的实时成像方式。在传统的波束形成方法中，B型超声图像含有散射源随机干扰引起的散斑噪声，影响了图像质量。

如图 7所示，Luchies和Byram(2018)将深度神经网络(deep neural network，DNN)应用于超声波束形成器中，先通过离散傅里叶变换(discrete Fourier transform, DFT)将超声信道数据变换到频域，训练针对不同频率的DNN来抑制离轴散射，再通过离散傅里叶变换的逆变换(inverse DFT, IDFT)进行恢复，达到提高超声成像质量的目的。Yoon等人(2019)则利用Rx-Xmit平面的冗余度直接插值射频信号丢失的射频数据，在不影响图像质量的前提下，有效提升了成像的速率。此外，也有学者提出针对噪声的抑制方法(Hyun等，2019)和基于模型感知的快速重建策略(Luijten等，2019)。这些研究都充分表明深度学习在传统B型超声成像中的潜在应用价值。

图 7 基于深度神经网络(DNN)的超声波束形成(Luchies和Byram，2018)

Fig. 7 Ultrasonic beamforming based on deep neaural network (DNN) (Luchies and Byram, 2018)

1.3.2 基于深度学习的平面波超声成像方法

1.3.3 基于深度学习的超声弹性成像方法

超声弹性成像已经逐步应用于乳腺癌、肝癌、甲状腺肿瘤等疾病的临床诊断。超声弹性成像能够重建出组织应变相关的图像，但目前的重建性能和效率还有待提升。基于深度学习的超声弹性成像研究也已经初步开展。例如，Wu等人(2018b)设计了一个端到端的CNN，直接从射频数据重建弹性信息。该网络仅用计算仿真作为训练数据，就可以从真实的射频数据中推断出弹性信息的分布。在后续的另一项工作中(Gao等，2019)，如图 8所示，将特权信息学习(learning using privileged information，LUPI)这一迁移学习模式应用于CNN中，以纠正网络学习的中间状态，并且提出了一种数据生成策略，以保证训练样本和标签中特权信息的因果关系，改善医学数据不足的问题。

图 8 基于特权信息学习的超声弹性图像重建流程(Gao等，2019)

Fig. 8 Flowchart of LUPI-based construction of ultrasound elastography images(Gao et al., 2019)

1.4 医学图像合成

1.4.1 跨模态MRI图像合成

目前的跨模态MRI图像合成主要研究从一个或几个MRI模态到其他缺失的一个或几个MRI模态的预测。深度学习模型，特别是GAN在跨模态MRI图像合成中获得了极大的应用(Mazurowski等，2019；Lundervold和Lundervold，2019)。

Chartsias等人(2018)提出了一种类似于U-Net的网络，利用输入模态约束得到统一的隐空间，结合肿瘤等区域的标注信息复原得到缺失的模态，提高了关键区域的合成质量。Yu等人(2019)提出Edge-Aware GANs (Ea-GANs)，如图 9所示，其网络由3个模块组成，包括生成器、判别器和边缘检测模块，原模态图像输入生成器以合成目标模态的图像，判别器用于判断输入的合成图像与真实图像的真伪，边缘检测模块负责将能够反映图像结构的边缘信息通过损失函数的形式结合进GAN。3个模块相互作用，实现了高质量的跨模态MRI图像合成。Dar等人(2019)提出一种新型条件GAN以实现T1至T2图像间的转换，在成对的模态数据上使用对抗损失函数保留了高频信息，使用感知损失函数增强了合成性能，同时，循环一致性损失函数也被应用于网络以充分利用未成对的模态数据的信息。Yang等人(2020b)提出了一种半监督的生成对抗网络实现双模态间的转换，其使用有监督的训练策略保证生成的目标模态与原模态图像间准确的空间相关性，同时使用无监督的训练方式为生成的图像提供真实且较原图像变化明显的视觉效果。

图 9 Ea-GANs流程图(Yu等，2019)

Fig. 9 Flowchart of Ea-GANs(Yu et al., 2019)

1.4.2 MR图像合成CT图像

与CT相比，MR图像不能直接提供必要的密度信息，但是扫描CT图像存在辐射，所以通过MR图像合成CT图像可以帮助解决诸如放疗计划等场景下的临床问题。

Xiang等人(2018)提出了一种改进的全卷积网络(fully convolutional networks，FCN)结构，完成了配对的脑部、前列腺T1图像到CT图像的合成，取得了铰传统的基于图谱方法更高精度的合成CT。此外，多种基于CycleGAN模型的网络相继提出，以解决训练数据中MRI图像与CT图像不匹配的问题。Wolterink等人(2017a)利用CyclaGAN模型在不成对的数据上实现了MRI-to-CT合成，取得了相比于利用成对数据的GAN模型更好的效果。Yang等人(2018b)提出了Structure-Constrained CyclGAN, 其中定义了一个结构一致性损失函数，以保证合成的CT图像与真实图像具有相似的结构。Hiasa等人(2018)提出了如图 10所示的网络结构，网络包含两组生成器与判别器，分别用于由CT图像生成MR图像以及由MR图像生成CT图像。由于输入的原模态图像没有对应的目标模态标签图像，网络利用循环一致性损失，将输入的原模态图像依次经过两个生成器再次生成该模态的合成图像，利用约束输入图像与合成图像间相似度的损失函数，优化两个生成器的网络参数，以保证单个生成器能有效地完成模态转换的任务。网络同时将对梯度的约束加入循环一致性损失函数中，以此提升生成图像的边缘准确性。

图 10 基于Cycle-GAN的MRI和CT图像流程图(Hiasa等，2018)

Fig. 10 Flowchart of Cycle-GAN for MRI-to-CT (Hiasa et al., 2018)

1.4.3 医学图像超分辨率

分辨率是MRI图像的一个重要指标，高分辨率MRI图像能提供更多的结构细节。然而，硬件条件、扫描时间等因素限制了MRI图像的分辨率。由于超分辨率方法属于图像后处理技术，应用较为方便，因此，基于超分辨率的MRI重建研究也是目前的一个重要方向。

MRI图像的超分辨率研究主要基于CNN实现。Pham等人(2017)使用基于SRCNN(super-resolution convolutional neural network)(Dong等，2014)的简单的3层网络结构由低分辨率MRI图像重建高分辨率图像，验证了CNN在MRI图像超分辨率任务上的有效性。之后，针对重建图像质量一般、结构和细节信息丢失、边缘模糊等问题，各种超分辨率网络对CNN从网络结构和损失函数策略等方面进行改进，提升了MRI超分辨率重建的性能(Shi等，2018a, 2019；Zhao等，2019b)。然而，由于缺乏真实采集的成对低分辨率和高分辨率的MRI图像，目前研究中的低分辨率图像主要是由真实的高辨率图像通过图像模糊和下采样等操作生成，导致目前研究算法缺乏真实数据集的验证。

另一方面，动脉自选标记(arterial spin labeling，ASL)MRI是一种非侵入式的衡量定量脑血流的成像技术，受动脉血液的纵向弛豫时间和标记后的传输时间等物理因素制约，高分辨率ASL MRI通常难以获得，传统方法获取高分辨率图像消耗大量时间，且伴随有很大噪声，因此针对ASL MRI的超分辨率研究具有较强的实际应用价值。Li等人(2019c)提出了一种两阶段多损失函数的CNN结构，如图 11所示，首先通过第1阶段的重建获取噪声较小的高分辨率重建图像，然后送入第2阶段网络以进一步优化其高频细节，最后在统一框架下进行整个网络的优化，通过加权融合生成高分辨率的ASL MRI图像。

图 11 两阶段多损失函数的超分辨率网络结构(Li等，2019c)

Fig. 11 Architecture of the two-stage multi-loss SR network (Li et al., 2019c)

2 病灶检测

2.1 基于CT的肺部病灶深度学习检测模型

2.2 面向肿瘤病灶的深度学习检测模型

2.3 基于深度学习的通用病灶区域检测模型

3 医学图像分割

3.1 全监督分割模型

3.2 非完全监督分割模型

3.3 融合先验知识的深度分割模型

4 医学图像配准

4.1 全监督配准模型

全监督的医学图像配准模型在训练阶段的每次迭代中，输入两幅图像，通过网络回归获得输出的空间变换参数(例如刚性变换矩阵、仿射变换矩阵、密集形变场等)(Eppenhof和Pluim，2019；Cao等，2018；Yang等，2017a；Fan等，2019b；Eppenhof等，2020)。通过在当前获得的空间变换参数与监督数据之间定义损失函数，可在空间变换所处的高维数据空间中计算误差，进而反向更新网络参数。这样的训练需要大量数据的参与，以训练可靠准确的网络模型。而在测试阶段，只需要输入待配准的两幅图像，就可以直接求得所需的空间变换参数。

全监督配准模型在训练时需要预先得到配准的空间变换参数，以作为网络优化的监督信息。Eppenhof和Pluim (2019)通过Bspline的方式采样得到密集形变场，并将该形变场作用于真实图像(如图 12)，得到新的变形后的图像。这样构成的一组浮动—固定图像及对应的密集形变场，可在全监督模式下训练U-Net网络。类似策略还包括Cao等人(2018)通过医学图像配准软件ANTs (advanced normalization tools)对图像预先进行配准，并将得到的形变场作为训练网络的ground truth。

图 12 基于有监督学习CNN的CT配准算法中的训练集数据准备流程(Eppenhof等，2020)

Fig. 12 Schematic explaining the on-the-fly training set construction in the CT registration through supervised learning with CNN (Eppenhof et al., 2020)

4.2 无监督配准模型

无监督的配准方法(Balakrishnan等，2019; de Vos等，2019)与传统方法的类似之处在于，两者都需要对图像的相似性加以定义，即网络的损失函数由图像相似性测度和空间变换约束(例如密集形变场的平滑程度)项组成。网络输出的空间变换参数，直接作用到待配准图像并得到形变后的图像；计算该图像域模板图像之间的相似性，并反向对网络进行优化。损失函数中的约束项一般直接定义在表示空间变换的参数空间，比如采用对形变场梯度的范数对形变场的剧烈变化加以削弱，以确保获得平滑的空间变换。训练阶段，通过大量的数据优化网络的参数；而在测试阶段，输入待配准的两个图像，直接得到形变场。

如图 13所示，Balakrishnan等人(2019)通过定义均方误差(mean square error, MSE)相似性测度和形变场平滑约束构成的损失函数，优化一个简单的U-Net网络，以无监督的方式训练得到了脑MR图像的配准模型VoxelMorph；de Vos等人(2019)通过无监督的训练方式叠加训练几个网络完成仿射、可形变的配准。

图 13 VoxelMorph配准流程图(Balakrishnan等, 2019)

Fig. 13 Flowchart of VoxelMorph registration (Balakrishnan et al., 2019)

4.3 弱监督配准模型

基于无监督模型的框架，弱监督(Hu等，2018)的配准方法在训练阶段引入现存的标注，以此引入额外的信息优化网络的参数配准精度，该方法在测试阶段不需要额外的标注信息。

Hu等人(2018)在训练阶段引入了前列腺区域的标注，通过计算形变后的标注数据和目标图像的标注数据优化网络的参数，进而完成了超声图像和磁共振图像的配准(如图 14所示)。基于弱监督模型的方式，可以帮助网络获取图像的生理解剖信息，进而提高多模态的配准。

图 14 弱监督模型训练策略(Hu等，2018)

Fig. 14 Training strategy of weakly-supervised registration framework (Hu et al., 2018)

基于上述3种配准模型的方法仍然面临着配准精度待提升、形变场交叉折叠、跨模态配准鲁棒性等问题，因此，也不断有其他的配准方法提出。例如，Zhao等人(2019a)提出了叠加网络的方法提高配准的精度，通过几个网络的叠加、形变参数的耦合，配准精度可以得到有效提升。Fan等人(2019b)通过结合定义好的图像域相似性度量和形变场的形似度量作为损失函数优化网络参数，进一步提高现有模型的配准精度。为了解决多模态、同模态的配准，Fan等人(2018, 2019a)提出通过GAN的方法，基于判别器来判断进行图像的测度。Dalca等人(2018)和Yang等人(2017a)提出通过网络估计速度场和动量场，并通过积分得到了形变场来完成微分同胚的变换。Shen等人(2019)通过提出自适应的方案解决自适应约束的问题，通过网络优化得到不同区域适应不同测度函数的形变参数进而在提高配准精度的同时降低了形变场的折叠情况。Wei等人(2020)提出通过预测中间图像的方式完成基于引导方式的图像配准，进而完成脑的分析、手术的引导，提高了配准的精度和鲁棒性。

总体而言，基于深度学习的医学图像配准方法在配准性能上有了较为显著的提升，其自身精度高、效率高等优势给了配准在手术导航等实际的临床问题中很大的发展空间(Miao等，2016)。但也存在着一定的局限性，包括对训练数据的要求、对硬件的要求等，在实际的应用中限制了其广泛的应用。基于深度学习的方法和传统的方法在目前的应用阶段各有所长，简化深度学习的训练数据可能会成为解决这一核心问题的关键。与此同时，传统配准方法面临的诸如鲁棒性等挑战，深度学习也尚未解决。

5 计算机辅助诊断

5.1 全监督深度学习模型

5.2 非完全监督模型

5.3 基于多模态影像的CAD

6 结语

参考文献

Aggarwal H K, Mani M P, Jacob M. 2019. MoDL:model-based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging, 38(2): 394-405 [DOI:10.1109/TMI.2018.2865356]
Akselrod-Ballin A, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R and Barkan E. 2016. A region based convolutional network for tumor detection and classification in breast mammography//Proceedings of the 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Athens: Springer: 197-205[DOI: 10.1007/978-3-319-46976-8_21]
Ambellan F, Tack A, Ehlke M, Zachow S. 2019. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks:data from the Osteoarthritis Initiative. Medical Image Analysis, 52: 109-118 [DOI:10.1016/j.media.2018.11.009]
Bai W J, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, Glocker B, King A, Matthews P M and Rueckert D. 2017. Semi-supervised learning for network-based cardiac MR image segmentation//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City: Springer: 253-260[DOI: 10.1007/978-3-319-66185-8_29]
Balakrishnan G, Zhao A, Sabuncu M R, Guttag J, Dalca A V. 2019. VoxelMorph:a learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging, 38(8): 1788-1800 [DOI:10.1109/TMI.2019.2897538]
Billones C D, Demetria O J L D, Hostallero D E D and Naval P C. 2016. DemNet: a convolutional neural network for the detection of Alzheimer's disease and mild cognitive impairment//Proceedings of 2016 IEEE Region 10 Conference. Singapore: IEEE: 3724-3727[DOI: 10.1109/TENCON.2016.7848755]
Burgos N, Cardoso M J, Thielemans K, Modat M, Pedemonte S, Dickson J, Barnes A, Ahmed R, Mahoney C J, Schott J M, Duncan J S, Atkinson D, Arridge S R, Hutton B F, Ourselin S. 2014. Attenuation correction synthesis for hybrid PET-MR scanners:application to brain studies. IEEE Transactions on Medical Imaging, 33(12): 2332-2341 [DOI:10.1109/TMI.2014.2340135]
Cai C B, Wang C, Zeng Y Q, Cai S H, Liang D, Wu Y W, Chen Z, Ding X H, Zhong J H. 2018. Single-shot T₂ mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network. Magnetic resonance in Medicine, 80(5): 2202-2214 [DOI:10.1002/mrm.27205]
Cai J Z, Zhang Z H, Cui L, Zheng Y F, Yang L. 2019. Towards cross-modal organ translation and segmentation:a cycle-and shape-consistent generative adversarial network. Medical Image Analysis, 52: 174-184 [DOI:10.1016/j.media.2018.12.002]
Cao X H, Yang J H, Zhang J, Wang Q, Yap P T, Shen D G. 2018. Deformable image registration using a cue-aware deep regression network. IEEE Transactions on Biomedical Engineering, 65(9): 1900-1911 [DOI:10.1109/TBME.2018.2822826]
Cao Z T, Yang G W, Chen Q, Chen X L, Lyu F M. 2020. Breast tumor classification through learning from noisy labeled ultrasound images. Medical Physics, 47(3): 1048-1057 [DOI:10.1002/mp.13966]
Carneiro G, Nascimento J and Bradley A P. 2015. Unregistered multiview mammogram analysis with pre-trained deep learning models//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 652-660[DOI: 10.1007/978-3-319-24574-4_78]
Chartsias A, Joyce T, Giuffrida M V, Tsaftaris S A. 2018. Multimodal MR synthesis via modality-invariant latent representation. IEEE Transactions on Medical Imaging, 37(3): 803-814 [DOI:10.1109/TMI.2017.2764326]
Chen C, Liu X P, Ding M, Zheng J F and Li J Y. 2019a. 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 184-192[DOI: 10.1007/978-3-030-32248-9_21]
Chen H, Zhang Y, Kalra M K, Lin F, Chen Y, Liao P X, Zhou J L, Wang G. 2017. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Transactions on Medical Imaging, 36(12): 2524-2535 [DOI:10.1109/TMI.2017.2715284]
Chen J, Yang G, Gao Z F, Ni H, Angelini E, Mohiaddin R, Wong T, Zhang Y P, Du X Q, Zhang H Y, Keegan J and Firmin D. 2018. Multiview two-task recursive attention model for left atrium and atrial scars segmentation//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 455-463[DOI: 10.1007/978-3-030-00934-2_51]
Chen X, Lian C F, Wang L, Deng H, Fung S H, Nie D, Thung K H, Yap P T, Gateno J, Xia J J, Shen D G. 2020. One-shot generative adversarial learning for MRI segmentation of craniomaxillofacial bony structures. IEEE Transactions on Medical Imaging, 39(3): 787-796 [DOI:10.1109/TMI.2019.2935409]
Chen X, Williams B M, Vallabhaneni S R, Czanner G, Williams R and Zheng Y L. 2019b. Learning active contour models for medical image segmentation//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE: 11624-11632[DOI: 10.1109/CVPR.2019.01190]
Chen Y X, Xiao T H, Li C, Liu Q G and Wang S S. 2019c. Model-based convolutional de-aliasing network learning for parallel MR imaging//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 30-38[DOI: 10.1007/978-3-030-32248-9_4]
Cheplygina V, de Bruijne M and Pluim J P W. 2019. Not-so-supervised:a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical Image Analysis, 54: 280-296 [DOI:10.1016/j.media.2019.03.009]
Choi H, Kim Y K, Yoon E J, Lee J Y, and Lee D S. 2020. Cognitive signature of brain FDG PET based on deep learning:domain transfer from Alzheimer's disease to Parkinson's disease. European Journal of Nuclear Medicine and Molecular Imaging, 47(2): 403-412 [DOI:10.1007/s00259-019-04538-7]
Dalca A V, Balakrishnan G, Guttag J and Sabuncu M R. 2018. Unsupervised learning for fast probabilistic diffeomorphic registration//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 729-738[DOI: 10.1007/978-3-030-00928-1_82]
Dar S U H, Yurt M, Karacan L, Erdem A, Erdem E, Çukur T. 2019. Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Transactions on Medical Imaging, 38(10): 2375-2388 [DOI:10.1109/TMI.2019.2901750]
de Vos B D, Berendsen F F, Viergever M A, Sokooti H, Staring M, Išgum I. 2019. A deep learning framework for unsupervised affine and deformable image registration. Medical Image Analysis, 52: 128-143 [DOI:10.1016/j.media.2018.11.010]
Degel MA, Navab N and Albarqouni S. 2018. Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 630-637[DOI: 10.1007/978-3-030-00937-3_72]
Deng L X, Tang S, Fu H Z, Wang B and Zhang Y D. 2019. Spatiotemporal breast mass detection network (MD-Net) in 4D DCE-MRI images//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 271-279[DOI: 10.1007/978-3-030-32251-9_30]
Ding J, Li A X, Hu Z Q and Wang L W. 2017. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City: Springer: 559-567[DOI: 10.1007/978-3-319-66179-7_64]
Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed I B. 2019. HyperDense-Net:a hyper-densely connected CNN for multi-modal image segmentation. IEEE Transactions on Medical Imaging, 38(5): 1116-1126 [DOI:10.1109/TMI.2018.2878669]
Dong C, Loy C C, He K M and Tang X O. 2014. Learning a deep convolutional network for image super-resolution//Proceedings of the 13th European Conference on Computer Vision. Zurich: Springer: 184-199[DOI: 10.1007/978-3-319-10593-2_13]
Dong D, Tang Z C, Wang S, Hui H, Gong L X, Lu Y, Xue Z, Liao H G, Chen F, Yang F, Jin R H, Wang K, Liu Z Y, Wei J W, Mu W, Zhang H, Jiang J Y, Tian J and Li H J. 2020. The role of imaging in the detection and management of COVID-19: a review. IEEE Reviews in Biomedical Engineering, [DOI: 10.1109/RBME.2020.2990959]
Dong S Y, Luo G N, Sun G X, Wang K Q and Zhang H G. 2016. A left ventricular segmentation method on 3D echocardiography using deep learning and snake//Proceedings of 2016 Computing in Cardiology Conference. Vancouver: IEEE: 473-476
Dou Q, Ouyang C, Chen C, Chen H, and Heng P A. 2018. Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm: AAAI Press: 691-697[DOI: 10.24963/ijcai.2018/96]
Du X Q, Song Y H, Liu Y G, Zhang Y P, Liu H, Chen B, Li S. 2020. An integrated deep learning framework for joint segmentation of blood pool and myocardium. Medical Image Analysis, 62: #101685 [DOI:10.1016/j.media.2020.101685]
Duan J M, Bello G, Schlemper J, Bai W J, Dawes T J W, Biffi C, de Marvao A, Doumoud G, O'Regan D P and Rueckert D, 2019. Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Transactions on Medical Imaging, 38(9): 2151-2164[DOI: 10.1109/TMI.2019.2894322]
Eppenhof K A J, Lafarge M W, Veta M, Pluim J P W. 2020. Progressively trained convolutional neural networks for deformable image registration. IEEE Transactions on Medical Imaging, 39(5): 1594-1604 [DOI:10.1109/TMI.2019.2953788]
Eppenhof K A J, Pluim J P W. 2019. Pulmonary CT registration through supervised learning with convolutional neural networks. IEEE Transactions on Medical Imaging, 38(5): 1097-1105 [DOI:10.1109/TMI.2018.2878316]
Fan J F, Cao X H, Wang Q, Yap P T, Shen D G. 2019a. Adversarial learning for mono-or multi-modal registration. Medical Image Analysis, 58: #101545 [DOI:10.1016/j.media.2019.101545]
Fan J F, Cao X H, Xue Z, Yap P T and Shen D G. 2018. Adversarial similarity network for evaluating image alignment in deep learning based registration//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 739-746[DOI: 10.1007/978-3-030-00928-1_83]
Fan J F, Cao X H, Yap P T, Shen D G. 2019b. BIRNet:brain image registration using dual-supervised fully convolutional networks. Medical Image Analysis, 54: 193-206 [DOI:10.1016/j.media.2019.03.006]
Fang L W, Zhang L C, Nie D, Cao X H, Rekik I, Lee S W, He H G, Shen D G. 2019. Automatic brain labeling via multi-atlas guided fully convolutional networks. Medical Image Analysis, 51: 157-168 [DOI:10.1016/j.media.2018.10.012]
Gao Z F, Wu S T, Liu Z, Luo J W, Zhang H Y, Gong M M, Li S. 2019. Learning the implicit strain reconstruction in ultrasound elastography using privileged information. Medical Image Analysis, 58: #101534 [DOI:10.1016/j.media.2019.101534]
Ghani M U and Karl W C. 2018. Deep learning-based sinogram completion for low-dose CT//Proceedings of the 13th IEEE Image, Video, and Multidimensional Signal Processing Workshop. Zagorochoria: IEEE: 1-5[DOI: 10.1109/IVMSPW.2018.8448403]
Hammernik K, Klatzer T, Kobler E, Recht M P, Sodickson D K, Pock T, Knoll F. 2018. Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 79(6): 3055-3071 [DOI:10.1002/mrm.26977]
Han Y S, Yoo J and Ye J C. 2016. Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis[EB/OL].[2020-05-17].https://arxiv.org/pdf/1611.06391.pdf
Han Z Y, Wei B Z, Mercado A, Leung S, Li S. 2018. Spine-GAN:semantic segmentation of multiple spinal structures. Medical Image Analysis, 50: 23-35 [DOI:10.1016/j.media.2018.08.005]
Haskins G, Kruger U, and Yan P K. 2020. Deep learning in medical image registration:a survey. Machine Vision and Applications, 31(1): #8 [DOI:10.1007/s00138-020-01060-x]
He K L, Cao X H, Shi Y H, Nie D, Gao Y, Shen D G. 2019. Pelvic organ segmentation using distinctive curve guided fully convolutional networks. IEEE Transactions on Medical Imaging, 38(2): 585-595 [DOI:10.1109/TMI.2018.2867837]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition.//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778[DOI: 10.1109/CVPR.2016.90]
Hiasa Y, Otake Y, Takao M, Matsuoka T, Takashima K, Carass A, Prince J L, Sugano N and Sato Y. 2018. Cross-modality image synthesis from unpaired data using CycleGAN: effects of gradient consistency loss and training data size//Proceedings of the 3rd International Workshop on Simulation and Synthesis in Medical Imaging. Granada: Springer: 31-41[DOI: 10.1007/978-3-030-00536-8_4]
Hu J, Shen L, Albanie S, Sun G, Wu E. 2020. Squeeze-and-Excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8): 2011-2023 [DOI:10.1109/TPAMI.2019.2913372]
Hu Y P, Modat M, Gibson E, Li W Q, Ghavami N, Bonmati E, Wang G T, Bandula S, Moore C M, Emberton M, Ourselin S, Noble J A, Barratt D C, Vercauteren T. 2018. Weakly-supervised convolutional neural networks for multimodal image registration. Medical Image Analysis, 49: 1-13 [DOI:10.1016/j.media.2018.07.002]
Hu Z L, Jiang C H, Sun F Y, Zhang Q Y, Ge Y S, Yang Y F, Liu X, Zheng H R, Liang D. 2019. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Medical Physics, 46(4): 1686-1696 [DOI:10.1002/mp.13415]
Humphries T, Si D, Coulter S, Simms M and Xing R W. 2019. Comparison of deep learning approaches to low dose CT using low intensity and sparse view data//Proceedings of SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging. San Diego: SPIE: #109484A[DOI: 10.1117/12.2512597]
Hyun D, Brickson L L, Looby K T, Dahl J J. 2019. Beamforming and speckle reduction using neural networks. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66(5): 898-910 [DOI:10.1109/TUFFC.2019.2903795]
Jia H Z, Song Y, Huang H, Cai W D and Xia Y. 2019a. HD-Net: hybrid discriminative network for prostate segmentation in MR images//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 110-118[DOI: 10.1007/978-3-030-32245-8_13]
Jia Z D, Yuan Z M and Peng J L. 2019b. Multimodal brain tumor segmentation using encoder-decoder with hierarchical separable convolution//Proceedings of the 4th International Workshop on MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019. Shenzhen: Springer: 130-138[DOI: 10.1007/978-3-030-33226-6_15]
Kang E, Chang W, Yoo J, Ye J C. 2018. Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Transactions on Medical Imaging, 37(6): 1358-1369 [DOI:10.1109/TMI.2018.2823756]
Kang E, Min J H, Ye J C. 2017a. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Medical Physics, 44(10): e360-e375 [DOI:10.1002/mp.12344]
Kang G X, Liu K, Hou B B, Zhang N B. 2017b. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One, 12(11): e0188290 [DOI:10.1371/journal.pone.0188290]
Kervadec H, Dolz J, Tang M, Granger E, Boykov Y, Ayed I B. 2019. Constrained-CNN losses for weakly supervised segmentation. Medical Image Analysis, 54: 88-99 [DOI:10.1016/j.media.2019.02.009]
Khened M, Kollerathu V A, Krishnamurthi G. 2019. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Medical Image Analysis, 51: 21-45 [DOI:10.1016/j.media.2018.10.004]
Khosravan N and Bagci U. 2018.S4 ND: single-shot single-scale lung nodule detection//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 794-802[DOI: 10.1007/978-3-030-00934-2_88]
Lee S G, Bae J S, Kim H, Kim J H and Yoon S. 2018. Liver lesion detection from weakly-labeled multi-phase CT volumes with a grouped single shot multibox detector//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 693-701[DOI: 10.1007/978-3-030-00934-2_77]
Li C, Sun H, Liu Z Y, Wang M Y, Zheng H R and Wang S S. 2019a. Learning cross-modal deep representations for multi-modal MR image segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 57-65[DOI: 10.1007/978-3-030-32245-8_7]
Li H L, Weng J, Shi Y J, Gu W R, Mao Y J, Wang Y H, Liu W W, Zhang J J. 2018. An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Scientific Reports, 8: #6600 [DOI:10.1038/s41598-018-25005-7]
Li Y, Meng F Q, Shi J. 2019b. Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease:a comparative study. Medical and Biological Engineering and Computing, 57(7): 1605-1616 [DOI:10.1007/s11517-019-01974-3]
Li Z, Liu Q P, Li Y R, Ge Q, Shang Y Q, Song D H, Wang Z, and Shi J. 2019c. A two-stage multi-loss super-resolution network for arterial spin labeling magnetic resonance imaging//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 12-20[DOI: 10.1007/978-3-030-32248-9_2]
Li Z H, Zhang S, Zhang J G, Huang K Q, Wang Y Z and Yu Y Z. 2019 d. MVP-Net: multi-view FPN with position-aware attention for deep universal lesion detection//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 13-21[DOI: 10.1007/978-3-030-32226-7_2]
Lian C F, Liu M X, Zhang J, Shen D G. 2020. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4): 880-893 [DOI:10.1109/TPAMI.2018.2889096]
Liang D, Cheng J, Ke Z W, Ying L. 2020. Deep magnetic resonance image reconstruction:inverse problems meet neural networks. IEEE Signal Processing Magazine, 37(1): 141-151 [DOI:10.1109/MSP.2019.2950557]
Liao H F, Huo Z M, Sehnert W J, Zhou S K and Luo J B. 2018. Adversarial sparse-view cbct artifact reduction//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 154-162[DOI: 10.1007/978-3-030-00928-1_18]
Liebgott H, Rodriguez-Molares A, Cervenansky F, Jensen J A and Bernard O. 2016. Plane-wave imaging challenge in medical ultrasound//Proceedings of 2016 IEEE International Ultrasonics Symposium. Tours: IEEE: 1-4[DOI: 10.1109/ULTSYM.2016.7728908]
Litjens G, Kooi T, Bejnordi B E, Setio A A A, Ciompi F, Ghafoorian M, van der Laak J A W M, van Ginneken B and Sánchez C I. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis, 42: 60-88 [DOI:10.1016/j.media.2017.07.005]
Liu K, Wang D, Rong M X. 2019. X-ray image classification algorithm based on semi-supervised generative adversarial networks. Acta Optica Sinica, 39(8): 0810003 (刘坤, 王典, 荣梦学. 2019. 基于半监督生成对抗网络X光图像分类算法. 光学学报, 39(8): 0810003) [DOI:10.3788/AOS201939.0810003]
Liu M H, Cheng D N, Yan W W. 2018a. Classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images. Frontiers in Neuroinformatics, 12: #35 [DOI:10.3389/fninf.2018.00035]
Liu M X, Zhang J, Adeli E, Shen D G. 2018b. Landmark-based deep multi-instance learning for brain disease diagnosis. Medical Image Analysis, 43: 157-168 [DOI:10.1016/j.media.2017.10.005]
Liu Q G, Yang Q X, Cheng H T, Wang S S, Zhang M H, Liang D. 2020. Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors. Magnetic Resonance in Medicine, 83(1): 322-336 [DOI:10.1002/mrm.27921]
Liu S F, Wang Y, Yang X, Lei B Y, Liu L, Li S X, Ni D, Wang T F. 2019a. Deep learning in medical ultrasound analysis:a review. Engineering, 5(2): 261-275 [DOI:10.1016/j.eng.2018.11.020]
Liu S Q, Liu S D, Cai W D, Che H Y, Pujol S, Kikinis R, Feng D G, Fulham M J. 2015. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Biomedical Engineering, 62(4): 1132-1140 [DOI:10.1109/TBME.2014.2372011]
Liu T J, Guo Q Q, Lian C F, Ren X H, Liang S J, Yu J, Niu L J, Sun W D, Shen D G. 2019b. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Medical Image Analysis, 58: #101555 [DOI:10.1016/j.media.2019.101555]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot multibox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer: 21-37[DOI: 10.1007/978-3-319-46448-0_2]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston: IEEE: 3431-3440[DOI: 10.1109/CVPR.2015.7298965]
Luchies A C, Byram B C. 2018. Deep neural networks for ultrasound beamforming. IEEE Transactions on Medical Imaging, 37(9): 2010-2021 [DOI:10.1109/TMI.2018.2809641]
Luijten B, Cohen R, de Bruijn F J, Schmeitz H A W, Mischi M, Eldar Y C and van Sloun R J G. 2019. Deep learning for fast adaptive beamforming//Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing. Brighton: IEEE: 1333-1337[DOI: 10.1109/ICASSP.2019.8683478]
Lundervold A S, Lundervold A. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29(2): 102-127 [DOI:10.1016/j.zemedi.2018.11.002]
Maicas G, Carneiro G, Bradley A P, Nascimento J C and Reid I. 2017. Deep reinforcement learning for active breast lesion detection from DCE-MRI//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City: Springer: 665-673[DOI: 10.1007/978-3-319-66179-7_76]
Man Y Z, Huang Y S B, Feng J Y, Li X, Wu F. 2019. Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net. IEEE Transactions on Medical Imaging, 38(8): 1971-1980 [DOI:10.1109/TMI.2019.2911588]
Marrone S, Piantadosi G, Fusco R, Petrillo A, Sansone M and Sansone C. 2017. An investigation of deep learning for lesions malignancy classification in breast DCE-MRI//Proceedings of the 19th International Conference on Image Analysis and Processing. Catania: Springer: 479-489[DOI: 10.1007/978-3-319-68548-9_44]
Mazurowski M A, Buda M, Saha A, Bashir M R. 2019. Deep learning in radiology:an overview of the concepts and a survey of the state of the art with focus on MRI. Journal of Magnetic Resonance Imaging, 49(4): 939-954 [DOI:10.1002/jmri.26534]
Miao S, Wang Z J, Liao R. 2016. A CNN regression approach for real-time 2D/3D registration. IEEE Transactions on Medical Imaging, 35(5): 1352-1363 [DOI:10.1109/TMI.2016.2521800]
Nair A A, Tran T D, Reiter A and Bell M A L. 2018. A deep learning based alternative to beamforming ultrasound images//Proceedings of 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary: IEEE: 3359-3363[DOI: 10.1109/ICASSP.2018.8461575]
Nair A A, Tran T D, Reiter A and Bell M A L. 2019a. A generative adversarial neural network for beamforming ultrasound images: invited presentation//Proceedings of the 53rd Annual Conference on Information Sciences and Systems. Baltimore: IEEE: 1-6[DOI: 10.1109/CISS.2019.8692835]
Nair A A, Tran T D, Reiter A and Bell M A L. 2019b. One-step deep learning approach to ultrasound image formation and image segmentation with a fully convolutional neural network//Proceedings of 2019 IEEE International Ultrasonics Symposium. Glasgow, United Kingdom: IEEE: 1481-1484[DOI: 10.1109/ULTSYM.2019.8925836]
Nie D, Gao Y Z, Wang L and Shen D G. 2018. ASDNet: attention based semi-supervised deep networks for medical image segmentation//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 370-378[DOI: 10.1007/978-3-030-00937-3_43]
Nie D, Wang L, Gao Y Z, Lian J, Shen D G. 2019. STRAINet:spatially varying stochastic residual adversarial networks for MRI pelvic organ segmentation. IEEE Transactions on Neural Networks and Learning Systems, 30(5): 1552-1564 [DOI:10.1109/TNNLS.2018.2870182]
Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W J, Caballero J, Cook S A, de Marvao A, Dawes T, O'Regan D P, Kainz B, Glocker B, Rueckert D. 2018. Anatomically constrained neural networks (ACNNs):application to cardiac image enhancement and segmentation. IEEE Transactions on Medical Imaging, 37(2): 384-395 [DOI:10.1109/TMI.2017.2743464]
Ouyang X, Xue Z, Zhan Y Q, Zhou X A, Wang Q F, Zhou Y, Wang Q and Cheng J Z. 2019. Weakly supervised segmentation framework with uncertainty: a study on pneumothorax segmentation in chest X-ray//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 613-621[DOI: 10.1007/978-3-030-32226-7_68]
Peng J Z, Estrada G, Pedersoli M, Desrosiers C. 2020. Deep cotraining for semi-supervised image segmentation. Pattern Recognition, 107: 107269 [DOI:10.1016/j.patcog.2020.107269]
Perdios D, Besson A, Arditi M and Thiran J P. 2017. A deep learning approach to ultrasound image recovery//Proceedings of 2017 IEEE International Ultrasonics Symposium. Washington: IEEE: 1-4[DOI: 10.1109/ULTSYM.2017.8092746]
Perslev M, Dam E B, Pai A and Igel C. 2019. One network to segment them all: a general, lightweight system for accurate 3D medical image segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 30-38[DOI: 10.1007/978-3-030-32245-8_4]
Pham C H, Ducournau A, Fablet R and Rousseau F. 2017. Brain MRI super-resolution using deep 3D convolutional networks//Proceedings of the 14th IEEE International Symposium on Biomedical Imaging. Melbourne: IEEE: 197-200[DOI: 10.1109/ISBI.2017.7950500]
Pinto A, Pereira S, Meier R, Alves V, Wiest R, Silva A C and Reyes M. 2018. Enhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome prediction//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Switzerland: Springer: 107-115[DOI: 10.1007/978-3-030-00931-1_13]
Rajchl M, Lee M C H, Oktay O, Kamnitsas K, Passerat-Palmbach J P, Bai W J, Damodaram M, Rutherford M A, Hajnal J V, Kainz B, Rueckert D. 2017. DeepCut:object segmentation from bounding box annotations using convolutional neural networks. IEEE Transactions on Medical Imaging, 36(2): 674-683 [DOI:10.1109/TMI.2016.2621185]
Ran M S, Xia W J, Huang Y Q, Lu Z X, Bao P, Liu Y, Sun H Q, Zhou J L and Zhang Y. 2020. MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI. IEEE Transactions on Radiation and Plasma Medical Sciences, (99): 1-1[DOI: 10.1109/TRPMS.2020.2991877]
Rasti R, Teshnehlab M, Phung S L. 2017. Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recognition, 72: 381-390 [DOI:10.1016/j.patcog.2017.08.004]
Ravishankar S, Bresler Y. 2011. MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Transactions on Medical Imaging, 30(5): 1028-1041 [DOI:10.1109/TMI.2010.2090538]
Redmon J and Farhadi A. 2018. Yolov3: an incremental improvement[EB/OL].[2020-05-17].https://arxiv.org/pdf/1804.02767.pdf
Ren S Q, He K M, Girshick R, Sun J. 2017. Faster R-CNN:towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149 [DOI:10.1109/TPAMI.2016.2577031]
Ronneberger O, Fischer P and Brox T. 2015. U-net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241[DOI: 10.1007/978-3-319-24574-4_28]
Roth H R, Lu L, Lay N, Harrison A P, Farag A, Sohn A, Summers R M. 2018. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Medical Image Analysis, 45: 94-107 [DOI:10.1016/j.media.2018.01.006]
Samala R K, Chan H P, Hadjiiski L M, Helvie M A, Cha K H, Richter C D. 2017. Multi-task transfer learning deep convolutional neural network:application to computer-aided diagnosis of breast cancer on mammograms. Physics in Medicine and Biology, 62(23): 8894-8908 [DOI:10.1088/1361-6560/aa93d4]
Sarraf S, DeSouza S S, Anderson J and Tofighi G. 2016. DeepAD: alzheimer's disease classification via deep convolutional neural networks using MRI and fMRI[EB/OL].[2020-05-17]. https://www.biorxiv.org/content/10.1101/070441v4.full.pdf
Schlemper J, Caballero J, Hajnal J V, Price A N, Rueckert D. 2018. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Transactions on Medical Imaging, 37(2): 491-503 [DOI:10.1109/TMI.2017.2760978]
Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D. 2019. Attention gated networks:learning to leverage salient regions in medical images. Medical Image Analysis, 53: 197-207 [DOI:10.1016/j.media.2019.01.012]
Shan H M, Padole A, Homayounieh F, Kruger U, Khera R D, Nitiwarangkul C, Kalra M K, Wang G. 2019. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nature Machine Intelligence, 1(6): 269-276 [DOI:10.1038/s42256-019-0057-9]
Shan H M, Zhang Y, Yang Q S, Kruger U, Kalra M K, Sun L, Cong W X, Wang G. 2018. 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Transactions on Medical Imaging, 37(6): 1522-1534 [DOI:10.1109/TMI.2018.2832217]
Shao Q B, Gong L J, Ma K, Liu H L and Zheng Y F. 2019. Attentive CT lesion detection using deep pyramid inference with multi-scale booster//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 301-309[DOI: 10.1007/978-3-030-32226-7_34]
Shen D G, Wu G R, Suk H I. 2017a. Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19: 221-248 [DOI:10.1146/annurev-bioeng-071516-044442]
Shen W, Zhou M, Yang F, Yu D D, Dong D, Yang C Y, Zang Y L, Tian J. 2017b. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61: 663-673 [DOI:10.1016/j.patcog.2016.05.029]
Shen Z Y, Vialard F X and Niethammer M. 2019. Region-specific diffeomorphic metric mapping//Proceedings of the 33rd Conference on Neural Information Processing Systems. Vancouver: Curran Associates, Inc: 1098-1108
Shi F, Wang J, Shi J, Wu Z Y, Wang Q, Tang Z Y, He K L, Shi Y H and Shen D G. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, (99): 1-13[DOI: 10.1109/RBME.2020.2987975]
Shi J, Li Z, Ying S H, Wang C F, Liu Q P, Zhang Q, Yan P K. 2019. MR image super-resolution via wide residual networks with fixed skip connection. IEEE Journal of Biomedical and Health Informatics, 23(3): 1129-1140 [DOI:10.1109/JBHI.2018.2843819]
Shi J, Liu Q P, Wang C F, Zhang Q, Ying S H, and Xu H Y. 2018a. Super-resolution reconstruction of MR image with a novel residual learning network algorithm. Physics in Medicine and Biology, 63(8): #085011 [DOI:10.1088/1361-6560/aab9e9]
Shi J, Zheng X, Li Y, Zhang Q, Ying S H. 2018b. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 22(1): 173-183 [DOI:10.1109/JBHI.2017.2655720]
Shi J, Zhou S C, Liu X, Zhang Q, Lu M H, Wang T F. 2016. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 194: 87-94 [DOI:10.1016/j.neucom.2016.01.074]
Shin H C, Roth H R, Gao M C, Lu L, Xu Z Y, Nogues I, Yao J H, Mollura D, Summers R M. 2016. Deep convolutional neural networks for computer-aided detection:CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5): 1285-1298 [DOI:10.1109/TMI.2016.2528162]
Shin S Y, Lee S, Yun I D, Kim S M, Lee K M. 2019. Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Transactions on Medical Imaging, 38(3): 762-774 [DOI:10.1109/TMI.2018.2872031]
Song W F, Li S, Liu J, Qin H, Zhang B, Zhang S Y, Hao A M. 2019. Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE Journal of Biomedical and Health Informatics, 23(3): 1215-1224 [DOI:10.1109/JBHI.2018.2852718]
Sotiras A, Davatzikos C, Paragios N. 2013. Deformable medical image registration:a survey. IEEE Transactions on Medical Imaging, 32(7): 1153-1190 [DOI:10.1109/TMI.2013.2265603]
Suk H I, Lee S W, Shen D G. 2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101: 569-582 [DOI:10.1016/j.neuroimage.2014.06.077]
Sun L Y, Fan Z W, Ding X H, Huang Y and Paisley J. 2019b. Joint CS-MRI reconstruction and segmentation with a unified deep network//Proceedings of the 26th International Conference on Information Processing in Medical Imaging. Hong Kong, China: Springer: 492-504[DOI: 10.1007/978-3-030-20351-1_38]
Sun L Y, Fan Z W, Fu X Y, Huang Y, Ding X H, Paisley J. 2019a. A deep information sharing network for multi-contrast compressed sensing MRI reconstruction. IEEE Transactions on Image Processing, 28(12): 6141-6153 [DOI:10.1109/TIP.2019.2925288]
Sun L Y, Fan Z W, Huang Y, Ding X H and Paisley J. 2018. Compressed sensing MRI using a recursive dilated network//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans: AAAI: 2444-2451
Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z. 2016. Rethinking the Inception architecture for computer vision//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE: 2818-2826[DOI: 10.1109/CVPR.2016.308]
Tajbakhsh N, Jeyaseelan L, Li Q, Chiang J N, Wu Z H, Ding X W. 2020. Embracing imperfect datasets:a review of deep learning solutions for medical image segmentation. Medical Image Analysis, 63: #101693 [DOI:10.1016/j.media.2020.101693]
Tang F, Liang S J, Zhong T, Huang X, Deng X G, Zhang Y, Zhou L H. 2020. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs. European Radiology, 30(2): 823-832 [DOI:10.1007/s00330-019-06441-z]
Tao Q Y, Ge Z Y, Cai J F, Yin J X and See S. 2019. Improving deep lesion detection using 3D contextual and spatial attention//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 185-193[DOI: 10.1007/978-3-030-32226-7_21]
Tong N, Gou S P, Yang S Y, Ruan D, Sheng K. 2018. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Medical Physics, 45(10): 4558-4567 [DOI:10.1002/mp.13147]
Wang J X, Li S, Song W F, Qin H, Zhang B and Hao A M. 2018a. Learning from weakly-labeled clinical data for automatic thyroid nodule classification in ultrasound images//Proceedings of the 25th IEEE International Conference on Image Processing. Athens: IEEE: 3114-3118[DOI: 10.1109/ICIP.2018.8451085]
Wang S, Nie D, Qu L Q, Shao Y Q, Lian J, Wang Q, Shen D G. 2020b. CT male pelvic organ segmentation via hybrid loss network with incomplete annotation. IEEE Transactions on Medical Imaging, 39(6): 2151-2162 [DOI:10.1109/TMI.2020.2966389]
Wang S S, Cheng H T, Ying L, Xiao T H, Ke Z W, Zheng H R, Liang D. 2020a. DeepcomplexMRI:exploiting deep residual network for fast parallel MR imaging with complex convolution. Magnetic Resonance Imaging, 68: 136-147 [DOI:10.1016/j.mri.2020.02.002]
Wang S S, Ke Z W, Cheng H T, Jia S, Ying L, Zheng H R and Liang D. 2019. DIMENSION: dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training. NMR in Biomedicine: #4131[DOI: 10.1002/nbm.4131]
Wang S S, Su Z H, Ying L, Peng X, Zhu S, Liang F, Feng D G and Liang D. 2016. Accelerating magnetic resonance imaging via deep learning//Proceedings of the 13th IEEE International Symposium on Biomedical Imaging. Prague: IEEE: 514-517[DOI: 10.1109/ISBI.2016.7493320]
Wang S S, Tan S, Gao Y, Liu Q G, Ying L, Xiao T H, Liu Y Y, Liu X, Zheng H R, Liang D. 2018b. Learning joint-sparse codes for calibration-free parallel MR imaging. IEEE Transactions on Medical Imaging, 37(1): 251-261 [DOI:10.1109/TMI.2017.2746086]
Wang Z W, Lin Y, Cheng K T, Yang X. 2020c. Semi-supervised mp-MRI data synthesis with stitchLayer and auxiliary distance maximization. Medical Image Analysis, 59: #101565 [DOI:10.1016/j.media.2019.101565]
Wen D, Wei Z H, Zhou Y H, Li G L, Zhang X, Han W. 2018. Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment:a brief overview and our opinion. Frontiers in Neuroinformatics, 12: #23 [DOI:10.3389/fninf.2018.00023]
Wei D M, Ahmad S, Huo J Y, Peng W, Ge Y H, Xue Z, Yap P T, Li W T, Shen D G and Wang Q. 2019. Synthesis and inpainting-based MR-CT registration for image-guided thermal ablation of liver tumors//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 512-520[DOI: 10.1007/978-3-030-32254-0_57]
Wei D M, Zhang L C, Wu Z W, Cao X H, Li G, Shen D G, Wang Q. 2020. Deep morphological simplification network (MS-Net) for guided registration of brain magnetic resonance images. Pattern Recognit, 100: #107171 [DOI:10.1016/j.patcog.2019.107171]
Wolterink J M, Dinkla A M, Savenije M H F, Seevinck P R, van den Berg C A T and Išgum I. 2017a. Deep MR to CT synthesis using unpaired data//Proceedings of the 2nd International Workshop on Simulation and Synthesis in Medical Imaging. Québec City: Springer: 14-23[DOI: 10.1007/978-3-319-68127-6_2]
Wolterink J M, Leiner T, Viergever M A, Išgum I. 2017b. Generative adversarial networks for noise reduction in low-dose CT. IEEE Transactions on Medical Imaging, 36(12): 2536-2545 [DOI:10.1109/TMI.2017.2708987]
Wu H F, Wu Y W, Sun L Y, Cai C B, Huang Y and Ding X H. 2018a. A deep ensemble network for compressed sensing MRI//Proceedings of the 25th International Conference on Neural Information Processing. Siem Reap: Springer: 162-171[DOI: 10.1007/978-3-030-04167-0_15]
Wu K, Du B W, Luo M, Wen H K, Shen Y R and Feng J F. 2019. Weakly supervised brain lesion segmentation via attentional representation learning//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 211-219[DOI: 10.1007/978-3-030-32248-9_24]
Wu S T, Gao Z F, Liu Z, Luo J W, Zhang H Y and Li S. 2018b. Direct reconstruction of ultrasound elastography using an end-to-end deep neural network//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 374-382[DOI: 10.1007/978-3-030-00928-1_43]
Xiang L, Chen Y, Chang W T, Zhan Y Q, Lin W L, Wang Q, Shen D G. 2019. Deep-learning-based multi-modal fusion for fast MR reconstruction. IEEE Transactions on Biomedical Engineering, 66(7): 2105-2114 [DOI:10.1109/TBME.2018.2883958]
Xiang L, Wang Q, Nie D, Zhang L C, Jin X Y, Qiao Y, Shen D G. 2018. Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Medical Image Analysis, 47: 31-44 [DOI:10.1016/j.media.2018.03.011]
Xiao L, Zhu C, Liu J J, Luo C L, Liu P F and Zhao Y. 2019. Learning from suspected target: bootstrapping performance for breast cancer detection in mammography//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 468-476[DOI: 10.1007/978-3-030-32226-7_52]
Xie H T, Yang D B, Sun N N, Chen Z N, Zhang Y D. 2019. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognition, 85: 109-119 [DOI:10.1016/j.patcog.2018.07.031]
Xie S N and Tu Z W. 2015. Holistically-nested edge detection//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago: IEEE: 1395-1403[DOI: 10.1109/ICCV.2015.164]
Xu H, Xie H T, Liu Y Z, Cheng C D, Niu C S and Zhang Y D. 2019. Deep cascaded attention network for multi-task brain tumor segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 420-428[DOI: 10.1007/978-3-030-32248-9_47]
Xue J, He K L, Nie D, Adeli E, Shi Z S, Lee S W, Zheng Y J, Liu X Y, Li D W, Shen D G. 2019. Cascaded multitask 3-D fully convolutional networks for pancreas segmentation. IEEE Transactions on Cybernetics, 99: 1-13 [DOI:10.1109/TCYB.2019.2955178]
Yan K, Bagheri M and Summers R M. 2018a. 3D context enhanced region-based convolutional neural network for end-to-end lesion detection//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 511-519[DOI: 10.1007/978-3-030-00928-1_58]
Yan K, Tang Y B, Peng Y F, Sandfort V, Bagheri M, Lu Z Y and Summers R M. 2019. Mulan: multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 194-202[DOI: 10.1007/978-3-030-32226-7_22]
Yan K, Wang X S, Lu L, Summers R M. 2018b. DeepLesion:automated mining of large-scale lesion annotations and universal lesion detection with deep learning. Journal of Medical Imaging, 5(3): #036501 [DOI:10.1117/1.JMI.5.3.036501]
Yang G, Yu S M, Dong H, Slabaugh G, Dragotti P L, Ye X, Liu F D, Arridge S, Keegan J, Guo Y K, Firmin D. 2018a. DAGAN:deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Transactions on Medical Imaging, 37(6): 1310-1321 [DOI:10.1109/TMI.2017.2785879]
Yang H R, Sun J, Carass A, Zhao C, Lee J, Xu Z B and Prince J. 2018b. Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN//Proceedings of the 4th International Workshop, DLMIA 2018, and the 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018. Granada: Springer: 174-182[DOI: 10.1007/978-3-030-00889-5_20]
Yang Q S, Yan P K, Zhang Y B, Yu H Y, Shi Y Y, Mou X Q, Kalra M K, Zhang Y, Sun L, Wang G. 2018c. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Transactions on Medical Imaging, 37(6): 1348-1357 [DOI:10.1109/TMI.2018.2827462]
Yang X, Dou H R, Li R, Wang X, Bian C, Li S L, Ni D and Heng P A. 2018d. Generalizing deep models for ultrasound image segmentation//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 497-505[DOI: 10.1007/978-3-030-00937-3_57]
Yang X, Kwitt R, Styner M, Niethammer M. 2017a. Quicksilver:fast predictive image registration-a deep learning approach. Neuroimage, 158: 378-396 [DOI:10.1016/j.neuroimage.2017.07.008]
Yang X, Lin Y, Wang Z W, Li X, Cheng K T. 2020b. Bi-modality medical image synthesis using semi-supervised sequential generative adversarial networks. IEEE Journal of Biomedical and Health Informatics, 24(3): 855-865 [DOI:10.1109/JBHI.2019.2922986]
Yang X, Yu L Q, Wu L Y, Wang Y, Ni D, Qin J and Heng P A. 2017b. Fine-grained recurrent neural networks for automatic prostate segmentation in ultrasound images//Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco: AAAI: 1633-1639
Yang Y, Sun J, Li H B and Xu Z B. 2016. Deep ADMM-Net for compressive sensing MRI//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: Curran Associates Inc: 10-18
Yang Y, Sun J, Li H B, Xu Z B. 2020a. ADMM-CSNet:a deep learning approach for image compressive sensing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3): 521-538 [DOI:10.1109/TPAMI.2018.2883941]
Yi X, Babyn P. 2018. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. Journal of Digital Imaging, 31(5): 655-669 [DOI:10.1007/s10278-018-0056-0]
Yi X, Walia E, Babyn P. 2019. Generative adversarial network in medical imaging:a review. Medical Image Analysis, 58: #101552 [DOI:10.1016/j.media.2019.101552]
Yin X R, Zhao Q L, Liu J, Yang W, Yang J, Quan G T, Chen Y, Shu H Z, Luo L M, Coatrieux J L. 2019. Domain progressive 3D residual convolution network to improve low-dose CT imaging. IEEE Transactions on Medical Imaging, 38(12): 2903-2913 [DOI:10.1109/TMI.2019.2917258]
Yoon Y H, Khan S, Huh J, Ye J C. 2019. Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning. IEEE Transactions on Medical Imaging, 38(2): 325-336 [DOI:10.1109/TMI.2018.2864821]
Yu B T, Zhou L P, Wang L, Shi Y H, Fripp J, Bourgeat P. 2019. Ea-GANs:edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Transactions on Medical Imaging, 38(7): 1750-1762 [DOI:10.1109/TMI.2019.2895894]
Zbontar J, Knoll F, Sriram A, Murrell T, Huang Z N, Muckley M J, Defazio A, Stern R, Johnson M, Bruno M, Parente M, Geras K J, Katsnelson J, Chandarana H, Zhang Z Z, Drozdzal M, Romero A, Rabbat M, Vincent P, Yakubova N, Pinkerton J, Wang D, Owens E, Zitnick C L, Recht M P, Sodickson D K and Lui Y W. 2018. fastMRI: an open dataset and benchmarks for accelerated MRI[EB/OL].[2020-05-01]. https://arxiv.org/pdf/1811.08839.pdf
Zeng W, Peng J, Wang S S, Liu Q G. 2020. A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond. Signal Processing:Image Communication, 81: #115701 [DOI:10.1016/j.image.2019.115701]
Zhang E L, Seiler S, Chen M L, Lu W G and Gu X J. 2019a. Boundary-aware semi-supervised deep learning for breast ultrasound computer-aided diagnosis//Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Berlin: IEEE: 947-950[DOI: 10.1109/EMBC.2019.8856539]
Zhang E L, Seiler S, Chen M L, Lu W G, Gu X J. 2020a. BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis. Physics in Medicine and Biology, 65(12): 125005 [DOI:10.1088/1361-6560/ab7e7d]
Zhang M H, Li M T, Zhou J J, Zhu Y J, Wang S S, Liang D, Chen Y, Liu Q G. 2020b. High-dimensional embedding network derived prior for compressive sensing MRI reconstruction. Medical Image Analysis, 64: #101717 [DOI:10.1016/j.media.2020.101717]
Zhang W L, Li R J, Deng H T, Wang L, Lin W L, Ji S W, Shen D G. 2015. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage, 108: 214-224 [DOI:10.1016/j.neuroimage.2014.12.061]
Zhang Z H, Zhang X S, Lin X N, Dong L C, Zhang S R, Zhang X L, Sun D S, Yuan K H. 2019c. Ultrasonic diagnosis of breast nodules using modified faster R-CNN. Ultrasonic Imaging, 41(6): 353-367 [DOI:10.1177/0161734619882683]
Zhang Z J, Fu H Z, Dai H, Shen J B, Pang Y W and Shao L. 2019b. ET-Net: a generic edge-attention guidance network for medical image segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 442-450[DOI: 10.1007/978-3-030-32239-7_49]
Zhao S Y, Dong Y, Chang E and Xu Y. 2019a. Recursive cascaded networks for unsupervised medical image registration//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE: 10599-10609[DOI: 10.1109/ICCV.2019.01070]
Zhao X L, Zhang Y L, Zhang T, Zou X M. 2019b. Channel splitting network for single MR image super-resolution. IEEE Transactions on Image Processing, 28(11): 5649-5662 [DOI:10.1109/TIP.2019.2921882]
Zhao X M, Wu Y H, Song G D, Li Z Y, Zhang Y Z, Fan Y. 2018. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 43: 98-111 [DOI:10.1016/j.media.2017.10.002]
Zhao Y X, Zhang Y M, Song M and Liu C L. 2019c. Multi-view semi-supervised 3D whole brain segmentation with a self-ensemble network//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 256-265[DOI: 10.1007/978-3-030-32248-9_29]
Zhou C H, Ding C X, Lu Z T, Wang X C and Tao D C. 2018. One-pass multi-task convolutional neural networks for efficient brain tumor segmentation//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 637-645[DOI: 10.1007/978-3-030-00931-1_73]
Zhou J, Luo L Y, Dou Q, Chen H, Chen C, Li G J, Jiang Z F, Heng P A. 2019a. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. Journal of Magnetic Resonance Imaging, 50(4): 1144-1151 [DOI:10.1002/jmri.26721]
Zhou S H, Nie D, Adeli E, Yin J P, Lian J, Shen D G. 2020a. High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Transactions on Image Processing, 29: 461-475 [DOI:10.1109/TIP.2019.2919937]
Zhou Y Y, Li Y W, Zhang Z S, Wang Y, Wang A T, Fishman E K, Yuille A L and Park S. 2019b. Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 155-163[DOI: 10.1007/978-3-030-32245-8_18]
Zhou Z W, Siddiquee M R, Tajbakhsh N, Liang J M. 2020b. UNet++:redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6): 1856-1867 [DOI:10.1109/TMI.2019.2959609]
Zhu B, Liu J Z, Cauley S F, Cauley S F, Rosen B R, Rosen M S. 2018a. Image reconstruction by domain-transform manifold learning. Nature, 555(7697): 487-492 [DOI:10.1038/nature25988]
Zhu W T, Liu C C, Fan W and Xie X H. 2018b. DeepLung: deep 3D dual path nets for automated pulmonary nodule detection and classification//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe: IEEE: 673-681[DOI: 10.1109/WACV.2018.00079]
Zhu W T, Vang Y S, Huang Y F and Xie X H. 2018c. DeepEM: deep 3D convnets with em for weakly supervised pulmonary nodule detection//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 812-820[DOI: 10.1007/978-3-030-00934-2_90]
Zlocha M, Dou Q and Glocker B. 2019. Improving retinaNet for CT lesion detection with dense masks from weak RECIST labels//Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen: Springer: 402-410[DOI: 10.1007/978-3-030-32226-7_45]