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收稿：2022-05-07，
修回：2022-8-11，
录用：2022-8-18，
纸质出版：2023-01-16
移动端阅览
目的
2
针对肝脏肿瘤检测方法对小尺寸肿瘤的检测能力较差和检测网络参数量过大的问题，在改进EfficientDet的基础上，提出用于肝脏肿瘤检测的多尺度自适应融合网络MAEfficientDet-D0(multiscale adaptive fusion network-D0)和MAEfficientDet-D1。
方法
2
首先，利用高效倒置瓶颈块替换EfficientDet骨干网络的移动倒置瓶颈块，在保证计算效率的同时，有效解决移动倒置瓶颈块的挤压激励网络维度和参数量较大的问题；其次，在特征融合网络前添加多尺度块，以扩大网络有效感受野，提高体积偏小病灶的检测能力；最后，提出多通路自适应加权特征融合块，以解决低层病灶特征图的语义偏弱和高层病灶特征图的细节感知能力较差的问题，提高了特征的利用率和增强模型对小尺寸肝脏肿瘤的检测能力。
结果
2
实验表明，高效倒置瓶颈层在少量增加网络复杂性的同时，可以有效提高网络对模糊图像的检测精度；多通路自适应加权特征融合模块可以有效融合含有上下文信息的深层特征和含有细节信息的浅层特征，进一步提高了模型对病灶特征的表达能力；多尺度自适应融合网络对肝脏肿瘤检测的效果明显优于对比模型。在LiTS(liver tumor segmentation)数据集上，MAEfficientDet-D0和MAEfficientDet-D1的mAP(mean average precision)分别为86.30%和87.39%；在3D-IRCADb(3D image reconstruction for comparison of algorithm database)数据集上，MAEfficientDet-D0和MAEfficientDet-D1的mAP分别为85.62%和86.46%。
结论
2
本文提出的MAEfficientDet系列网络提高了特征的利用率和小病灶的检测能力。相比主流检测网络，本文算法具有较好的检测精度和更少的参数量、计算量和运行时间，对肝脏肿瘤检测模型部署于嵌入式设备和移动终端设备具有重要参考价值。
Objective
2
Human liver-detected computerized tomography (CT) images are widely used in the diagnosis of liver diseases. CT images-based liver tumors' symptom is varied in related to its shape
size and location and its low contrast is projected with adjacent tissues. However
the challenging issues are concerned of poor detection ability of small-sized tumors in liver tumor detection and a huge number of parameters of detection network. These challenges are mainly involved in as mentioned below: 1) weak detection ability of small lesions; 2) large amount of model parameters-derived low efficiency and high computational cost; 3) less semantic feature description ability of the model for low-level feature map lesions; 4) poor details-perceptive ability for high-level feature map lesions. In order to solve these problems and improve the detection and recognition ability of the model
we develop a multi-scale EfficientDet-based adaptive fusion network (MAEfficientDet-D0
MAEfficientDet-D1) for liver tumor detection.
Method
2
A multiscale adaptive fusion network method
called MAEfficientDet
is facilitated for liver tumor detection. Our contribitions are based the key asepcts as following: 1) first
efficient inverted bottleneck (EFConv) is designed to replace the mobile inverted bottleneck block of EfficientDet backbone network with efficient inverted bottleneck block
which can effectively reolve the problem of large dimensions and parameters of the squeeze excitation network of mobile-inverted bottleneck block. The structure of the EFConv is to construct multi-channel of input image by expanding convolution to obtain more feature layers. Next
the depth separable convolution is used to extract the features of each layer. Third
a local channel-across interaction strategy with no dimension-reduced is used to realize channel-cross information interaction
and one-dimensional convolution is used to reduce the complexity of the model significantly. Fourth
the number of channels is compressed by dimensional-reduced convolution. Finally
the residual connection is used to alleviate the gradient dispersion and improve the parameter-transfering ability for network model training efficiency. 2) The multiscale blocks (Multiscale-A
Multiscale-B) are focused regional features of liver lesions to expand the effective receptive field of the network and improve the detection ability of small lesions. The internal structure of multi-scale blocks can be divided into multi-branch convolution layers of different cores and maximum pooling operations. Its characteristics are illustrated below: (1) adopting 1 × 1 convolution filtering useless information; (2) using different convolution kernels for different branches to obtain characteristic graphs of different sizes; (3) using the maximum pool operation of different receptive fields to reduce the size of the characteristic map and prevent the network from over fitting; (4) using residuals to improve the efficiency of network parameter transmission. 3) Using multi-channel adaptive weighted feature fusion block (MAWFF) to adaptively fuse the high-level semantic features and the low-level fine-grained features of the liver tumor image. The problems of weak semantics of the low-level lesion feature map and poor details perception of the high-level lesion feature map can be resolved further and the utilization of features and the detection ability of the model are improved. The experimental datasets are composed of liver tumor segmentation challenge dataset (LiTS) and 3D image reconstruction for comparison of algorithm database (3D-IRCADb).
Result
2
The experiments show that the efficient inversion of bottleneck layer can improve the detection accuracy of fuzzy images effectively while improving a small amount of network complexity. The multi-channel adaptive feature-weighted fusion module fuses the deep features-contextual information effectively and the features-shallowed detail information
which improves the demonstration ability of the model to the lesion features further. The effect of multi-scale adaptive fusion network on liver tumor detection is significantly developed and optimized in terms of the comparative analyses as listed below: 1) LiTS-based: MAEfficientDet-D0 is higher than EfficientDet-D0 by 7.48%
9.57%
6.42%
7.96% and 8.52%
respectively. MAEfficientDet-D1 is increased by 3.47%
6.64%
6.33%
8.12% and 5.02% of each beyond EfficientDet-D1. 2) 3D-IRCADb-based: MAEfficientDet-D0 is increased by 5.51%
9.82%
6.16%
7.39% and 7.63%
respectively beyond EfficientDet-D0. MAEfficientDet-D1 is increased by 5.87%
6.24%
5.81%
9.39% and 6.05%
respectively beyond EfficientDet-D1.
Conclusion
2
Our MAEfficientDet-D0 and MAEfficientDet-D1 architectures improve the utilization of features and the detection ability of small lesions. Our detection algorithm has better results on detection accuracy
less parameter amount
calculation cost and running time
as well as its potentials for embedded devices and mobile terminal devices.
Bilic P, Christ P F, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu C W, Han X, Heng P A, Hesser J, Kadoury S, Konopczynski T, Le M, Li C M, Li X M, LipkovàJ, Lowengrub J, Meine H, Moltz J H, Pal C, Piraud M, Qi X J, Qi J, Rempfler M, Roth K, Schenk A, Sekuboyina A, Vorontsov E, Zhou P, Hülsemeyer C, Beetz M, Ettlinger F, Gruen F, Kaissis G, Loh fer F, Braren R, Holch J, Hofmann F, Sommer W, Heinemann V, Jacobs C, Mamani G E H, van Ginneken B, Chartrand G, Tang A, Drozdzal M, Ben-Cohen A, Klang E, Amitai M M, Konen E, Greenspan H, Moreau J, Hostettler A, Soler L, Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L and Menze B H. 2019. The liver tumor segmentation Benchmark (LiTS)[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/1901.04056.pdf https://arxiv.org/pdf/1901.04056.pdf
Bochkovskiy A, Wang C Y and Liao H Y M. 2020. Yolov4: optimal speed and accuracy of object detection[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/2004.10934.pdf https://arxiv.org/pdf/2004.10934.pdf
Che H, Brown L G, Foran D J, Nosher J L and Hacihaliloglu I. 2021. Liver disease classification from ultrasound using multi-scale CNN. International Journal of Computer Assisted Radiology and Surgery, 16(9): 1537-1548[DOI: 10.1007/s11548-021-02414-0]
Guo X Y, Wang F S, Teodoro G, Farris A B and Kong J. 2019. Liver steatosis segmentation with deep learning methods//The 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy: IEEE: 24-27[ DOI: 10.1109/ISBI.2019.8759600 http://dx.doi.org/10.1109/ISBI.2019.8759600 ]
He K M, Gkioxari G, Dollár P and Girshick R. 2017.Mask R-CNN//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE: 2980-2988[ DOI: 10.1109/ICCV.2017.322 http://dx.doi.org/10.1109/ICCV.2017.322 ]
Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, Andreetto M and Adam H. 2017. Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/1704.04861.pdf https://arxiv.org/pdf/1704.04861.pdf
Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7132-7141[ DOI: 10.1109/CVPR.2018.00745 http://dx.doi.org/10.1109/CVPR.2018.00745 ]
Ircad France. 2020. 3Dircadb[EB/OL ] . [2022-04-27 ] . https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01 https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01
Kesav N and Jibukumar M G. 2022. Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. Journal of King Saud University-Computer and Information Sciences, 34(8): 6229-6242[DOI: 10.1016/j.jksuci.2021.05.008]
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, Spain: Springer: 693-701[ DOI: 10.1007/978-3-030-00934-2_77 http://dx.doi.org/10.1007/978-3-030-00934-2_77 ]
Lin T Y, Dollar P, Girshick R, He K M, Hariharan B and Belongie S. 2017a. Feature pyramid networks for object detection//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 936-944[ DOI: 10.1109/cvpr.2017.106 http://dx.doi.org/10.1109/cvpr.2017.106 ]
Lin T Y, Goyal P, Girs hick R, He K M and Dollár P. 2017b. Focal loss for dense object detection//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2999-3007[ DOI: 10.1109/ICCV.2017.324 http://dx.doi.org/10.1109/ICCV.2017.324 ]
Liu L, Ouyang W L, Wang X G, Fieguth P, Chen J, Liu X W and Pietikäinen M. 2020. Deep learning for generic object detection: a survey. International Journal of Computer Vision, 128(2): 261-318[DOI: 10.1007/s11263-019-01247-4]
Liu S T, Huang D and Wang Y H. 2019. Learning spatial fusion for single-shot object detection[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/1911.09516.pdf https://arxiv.org/pdf/1911.09516.pdf
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, the Netherlands: Springer: 21-37[ DOI: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ]
Ma J C, Song Y, Tian X, Hua Y T, Zhang R G and Wu J L. 2020. Survey on deep learning for pulmonary medical imaging. Frontiers of Medicine, 14(4): 450-469[DOI: 10.1007/s11684-019-0726-4]
Redmon J and Farhadi A. 2018. YOLOv3: an incremental improvement[EB/OL ] . [2022-04-27 ] . 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. 2015. Faster R-CNN: towards real-time object detection with region proposal networks//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press: 91-99
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, USA: IEEE: 2818-2826[ DOI: 10.1109/CVPR.2016.308 http://dx.doi.org/10.1109/CVPR.2016.308 ]
Tan M X, Pang R M and Le Q V. 2020. EfficientDet: scalable and efficient object detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 10778-10787[ DOI: 10.1109/CVPR42600.2020.01079 http://dx.doi.org/10.1109/CVPR42600.2020.01079 ]
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 Computin g and Computer Assisted Intervention. Shenzhen, China: Springer: 185-193[ DOI: 10.1007/978-3-030-32226-7_21 http://dx.doi.org/10.1007/978-3-030-32226-7_21 ]
Xie X X, Cheng G, Yao Y Q, Yao X W and Han J W. 2022. Dynamic feature fusion for object detection in remote sensing images. Chinese Journal of Computers, 45(4): 735-747
谢星星, 程塨, 姚艳清, 姚西文, 韩军伟. 2022. 动态特征融合的遥感图像目标检测. 计算机学报, 45(4): 735-747[DOI: 10.11897/SP.J.1016.2022.00735]
Yu W Q, Yu J, Bai M Y and Xiao C B. 2021. Video object detection using fusion of SSD and spatiotemporal features. Journal of Image and Graphics, 26(3): 542-555
尉婉青, 禹晶, 柏鳗晏, 肖创柏. 2021. SSD与时空特征融合的视频目标检测. 中国图象图形学报, 26(3): 542-555[DOI: 10.11834/jig.200020]
Zhang Z L, Li Y F, Wu W, Chen H J, Cheng L and Wang S. 2021. Tumor detection using deep learning method in automated breast ultrasound. Biomedical Signal Processing and Control, 68: #102677[DOI: 10.1016/j.bspc.2021.102677]
Zhou X Y, Wang D Q and Krähenbühl P. 2019. Objects as points[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/1904.07850.pdf https://arxiv.org/pdf/1904.07850.pdf
Zoph B and Le Q V. 2017. Neural architecture search with reinforcement learning[EB/OL ] . [2022-04-27 ] . https://arxiv.org/pdf/1611.01578.pdf https://arxiv.org/pdf/1611.01578.pdf
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