浏览全部资源
扫码关注微信
Received：02 June 2020，
Revised：2020-7-3，
Accepted：10 July 2020，
Published：16 October 2020
移动端阅览
深度学习能自动从大样本数据中学习获得优良的特征表达，有效提升各种机器学习任务的性能，已广泛应用于信号处理、计算机视觉和自然语言处理等诸多领域。基于深度学习的医学影像智能计算是目前智慧医疗领域的研究热点，其中深度学习方法已经应用于医学影像处理、分析的全流程。由于医学影像内在的特殊性、复杂性，特别是考虑到医学影像领域普遍存在的小样本问题，相关学习任务和应用场景对深度学习方法提出了新要求。本文以临床常用的X射线、超声、计算机断层扫描和磁共振等4种影像为例，对深度学习在医学影像中的应用现状进行综述，特别面向图像重建、病灶检测、图像分割、图像配准和计算机辅助诊断这5大任务的主要深度学习方法的进展进行介绍，并对发展趋势进行展望。
Deep learning can automatically learn from a large amount of data to obtain effective feature representations
thereby effectively improving the performance of various machine learning tasks. It has been widely used in various fields of medical imaging. Smart healthcare has become an important application area of deep learning
which is an effective approach to solve the following clinical problems: 1) given the limited medical resources
the experienced radiologists are not fully available
which cannot satisfy the fast development of the clinical requirement; 2) the lack of experienced radiologists
which cannot satisfy the fast increase of medical demand. At present
deep learning-based intelligent medical imaging systems are the typical scenarios in smart healthcare. This paper primarily reviews the applications of deep learning methods in various applications using four major clinical imaging techniques (i.e.
X-ray
ultrasound
computed tomography(CT)
and magnetic resonance imaging(MRI)). These works cover the whole pipeline of medical imaging
including reconstruction
detection
segmentation
registration
and computer-aided diagnosis (CAD). The reviews on medical image reconstruction focus on both MRI reconstruction and low-dose CT reconstruction on the basis of deep learning. Deep learning methods for MRI reconstruction can be divided into two categories: 1) data-driven end-to-end methods
2) model-based methods. The low-dose CT reconstruction primarily introduces methods on the basis of convolutional neural networks and generative adversarial networks. In addition
deep learning methods for ultrasound imaging
medical image synthesis
and medical image super-resolution are reviewed. The reviews on lesion detection primarily focuses on the deep learning methods for lung lesions detection using CT
the deep learning detection model for tumor lesions
and the deep learning methods for the general lesion area detection. At present
deep learning has been widely used in medical image segmentation tasks
and its performance is significantly improved compared with traditional image segmentation methods. Most deep learning segmentation methods are typical data-driven machine learning models. We review supervised models
semi-supervised models
and self-supervised models with regard to the amount of labeled data and annotation. Medical images contain rich anatomical information
which enhances the performance of deep learning models with different supervision. Deep learning models incorporating prior knowledge are also reviewed. Medical image registration consistency is a difficult task in the field of medical image analysis. Deep learning has become a breakthrough to improve the performance of medical image registration. The end-to-end network structures produce high-precision registration results and have become a hotspot in the field of image registration. Compared with the conventional methods
the deep learning methods for medical image registration have a significant improvement in registration performance. According to the different supervision in the training procedure
this paper divides the deep learning methods for medical image registration into three modes: fully supervised methods
unsupervised methods
and weakly supervised methods. Computer-aided diagnosis is another application of deep learning in the field of medical imaging. This paper summarizes the deep learning methods on CAD with different supervision and the CAD works on the basis of multi-modality medical images. Notably
although deep learning methods have been applied in medical imaging
several challenges are still identified. For example
the small-sample size problem is common in medical imaging analysis. Advanced machine learning methods
including weakly supervised learning
transfer learning
few-shot learning
self-supervised learning
and increase learning
can help alleviate this problem. In addition
the data annotation of medical images is a problem that seriously restricts the extensive and in-depth application of deep learning
and extensive research on automatic data labeling must be carried out. Interpretability of the deep neural networks is also important in medical image analysis. Improving the interpretability of a deep neural network has always been a difficult point
and in-depth research must be carried out in this area. Furthermore
carrying out human-computer collaboration in medical care is important. The lightweight deep neural network is easy to deploy into portable medical devices
giving portable devices more powerful functions
which is also an important research direction. Deep learning has been successful in various tasks in medical imaging analysis. New methods must be developed for its further application in intelligent medical products.
Aggarwal H K, Mani M P and 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 http://dx.doi.org/10.1007/978-3-319-46976-8_21 ]
Ambellan F, Tack A, Ehlke M and 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 http://dx.doi.org/10.1007/978-3-319-66185-8_29 ]
Balakrishnan G, Zhao A, Sabuncu M R, Guttag J and 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 http://dx.doi.org/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 and 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 and Zhong J H. 2018. Single-shot T 2 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 and 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 and 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 and 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 http://dx.doi.org/10.1007/978-3-319-24574-4_78 ]
Chartsias A, Joyce T, Giuffrida M V and 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 http://dx.doi.org/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 and 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 http://dx.doi.org/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 and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-00928-1_82 ]
Dar S U H, Yurt M, Karacan L, Erdem A, Erdem E and 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 and 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 http://dx.doi.org/10.1007/978-3-030-00937-3_72 ]
Deng L X, Tang S, Fu H Z, WangB 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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-319-66179-7_64 ]
Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/10.24963/ijcai.2018/96 ]
Du X Q, Song Y H, Liu Y G, Zhang Y P, Liu H, Chen B and 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 http://dx.doi.org/10.1109/TMI.2019.2894322 ]
Eppenhof K A J, Lafarge M W, Veta M and 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 and 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 and 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 http://dx.doi.org/10.1007/978-3-030-00928-1_83 ]
Fan J F, Cao X H, Yap P T and 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 and 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 and 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 http://dx.doi.org/10.1109/IVMSPW.2018.8448403 ]
Hammernik K, Klatzer T, Kobler E, Recht M P, Sodickson D K, Pock T and 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 https://arxiv.org/pdf/1611.06391.pdf
Han Z Y, Wei B Z, Mercado A, Leung S and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-00536-8_4 ]
Hu J, Shen L, Albanie S, Sun G and 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 and 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 and 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 http://dx.doi.org/10.1117/12.2512597 ]
Hyun D, Brickson L L, Looby K T and 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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-33226-6_15 ]
Kang E, Chang W, Yoo J and 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 and 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 and 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 and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/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 and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-32226-7_2 ]
Lian C F, Liu M X, Zhang J and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/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 and Rong M X. 2019. X-ray image classification algorithm based on semi-supervised generative adversarial networks. Acta Optica Sinica, 39(8):0810003[DOI:10.3788/AOS201939.0810003]
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 and 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 and 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 and 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 and 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 and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/10.1109/CVPR.2015.7298965 ]
Luchies A C and 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 http://dx.doi.org/10.1109/ICASSP.2019.8683478 ]
Lundervold A S and 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 http://dx.doi.org/10.1007/978-3-319-66179-7_76 ]
Man Y Z, Huang Y S B, Feng J Y, Li X and 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 http://dx.doi.org/10.1007/978-3-319-68548-9_44 ]
Mazurowski M A, Buda M, Saha A and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-00937-3_43 ]
Nie D, Wang L, Gao Y Z, Lian J and 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 and 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 http://dx.doi.org/10.1007/978-3-030-32226-7_68 ]
Peng J Z, Estrada G, Pedersoli M and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/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 and 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 http://dx.doi.org/10.1109/TRPMS.2020.2991877 ]
Rasti R, Teshnehlab M and 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 and 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 https://arxiv.org/pdf/1804.02767.pdf
Ren S Q, He K M, Girshick R and 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 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Roth H R, Lu L, Lay N, Harrison A P, Farag A, Sohn A and 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 and 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/aa93 d4]
Sarraf S, DeSouza S S, Anderson J and Tofighi G. 2016. DeepAD: alzheimer's diseaseclassification via deep convolutional neural networks using MRI and fMRI[EB/OL].[2020-05-17] . https://www.biorxiv.org/content/10.1101/070441v4.full.pdf https://www.biorxiv.org/content/10.1101/070441v4.full.pdf
Schlemper J, Caballero J, Hajnal J V, Price A N and 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 and 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 and 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 and 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 http://dx.doi.org/10.1007/978-3-030-32226-7_34 ]
Shen D G, Wu G R and 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 and 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 http://dx.doi.org/10.1109/RBME.2020.2987975 ]
Shi J, Li Z, Ying S H, Wang C F, Liu Q P, Zhang Q and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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-MRIreconstruction 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 http://dx.doi.org/10.1007/978-3-030-20351-1_38 ]
Sun L Y, Fan Z W, Fu X Y, Huang Y, Ding X H and 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 http://dx.doi.org/10.1109/CVPR.2016.308 ]
Tajbakhsh N, Jeyaseelan L, Li Q, Chiang J N, Wu Z H and 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 and 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 http://dx.doi.org/10.1007/978-3-030-32226-7_21 ]
Tong N, Gou S P, Yang S Y, Ruan D and 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 http://dx.doi.org/10.1109/ICIP.2018.8451085 ]
Wang S, Nie D, Qu L Q, Shao Y Q, Lian J, Wang Q and 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 andLiang 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 http://dx.doi.org/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 http://dx.doi.org/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 and 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 and 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 and 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 http://dx.doi.org/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 and 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 http://dx.doi.org/10.1007/978-3-319-68127-6_2 ]
Wolterink J M, Leiner T, Viergever M A and 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 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-00928-1_43 ]
Xiang L, Chen Y, Chang W T, Zhan Y Q, Lin W L, Wang Q and 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 and 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 http://dx.doi.org/10.1007/978-3-030-32226-7_52 ]
Xie H T, Yang D B, Sun N N, Chen Z N and 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 http://dx.doi.org/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 http://dx.doi.org/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 and 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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-32226-7_22 ]
Yan K, Wang X S, Lu L and 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 and 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 http://dx.doi.org/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 and 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 http://dx.doi.org/10.1007/978-3-030-00937-3_57 ]
Yang X, Kwitt R, Styner M and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 https://arxiv.org/pdf/1811.08839.pdf
Zeng W, Peng J, Wang S S and 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 http://dx.doi.org/10.1109/EMBC.2019.8856539 ]
Zhang E L, Seiler S, Chen M L, Lu W G and 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/ab7e7 d]
Zhang M H, Li M T, Zhou J J, Zhu Y J, Wang S S, Liang D, Chen Y and 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 and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/10.1109/ICCV.2019.01070 ]
Zhao X L, Zhang Y L, Zhang T and 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 and 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 http://dx.doi.org/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 http://dx.doi.org/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 and 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 and 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 http://dx.doi.org/10.1007/978-3-030-32245-8_18 ]
Zhou Z W, Siddiquee M R, Tajbakhsh N and 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 and 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 detectionand classification//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe: IEEE: 673-681[ DOI: 10.1109/WACV.2018.00079 http://dx.doi.org/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 http://dx.doi.org/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 http://dx.doi.org/10.1007/978-3-030-32226-7_45 ]
相关文章
相关作者
相关机构
京公网安备11010802024621
