Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 316-324.DOI: 10.11772/j.issn.1001-9081.2021010200
• Frontier and comprehensive applications • Previous Articles
Jie WU1,2, Shitian ZHANG3, Haibin XIE1,2, Guang YANG1,2()
Received:
2021-02-03
Revised:
2021-04-28
Accepted:
2020-04-29
Online:
2021-05-07
Published:
2022-01-10
Contact:
Guang YANG
About author:
WU Jie, born in 1995, M. S. candidate. Her research interests include machine learning, medical image processing.Supported by:
通讯作者:
杨光
作者简介:
吴洁(1995—),女,江苏扬州人,硕士研究生,主要研究方向:机器学习、医学图像处理基金资助:
CLC Number:
Jie WU, Shitian ZHANG, Haibin XIE, Guang YANG. Semi-supervised knee abnormality classification based on multi-imaging center MRI data[J]. Journal of Computer Applications, 2022, 42(1): 316-324.
吴洁, 张师天, 谢海滨, 杨光. 基于多影像中心磁共振成像数据的半监督膝盖异常分类[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 316-324.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021010200
数据集 | 膝盖正常 | 膝盖异常 | 合计 | |||
---|---|---|---|---|---|---|
前交叉韧带撕裂 | 半月板撕裂 | 前交叉韧带与半月板都撕裂 | 其他膝盖异常 | |||
MRNet训练集 | 217 | 83 | 272 | 125 | 433 | 1 130 |
MRNet验证集 | 25 | 23 | 21 | 31 | 20 | 120 |
本文训练集 | 193 | 59 | 248 | 101 | 409 | 1 010 |
本文验证集 | 24 | 24 | 24 | 24 | 24 | 120 |
Tab. 1 Distribution of original training set and validation set of MRNet dataset and training set and validation set used in the paper
数据集 | 膝盖正常 | 膝盖异常 | 合计 | |||
---|---|---|---|---|---|---|
前交叉韧带撕裂 | 半月板撕裂 | 前交叉韧带与半月板都撕裂 | 其他膝盖异常 | |||
MRNet训练集 | 217 | 83 | 272 | 125 | 433 | 1 130 |
MRNet验证集 | 25 | 23 | 21 | 31 | 20 | 120 |
本文训练集 | 193 | 59 | 248 | 101 | 409 | 1 010 |
本文验证集 | 24 | 24 | 24 | 24 | 24 | 120 |
等级 | 颜色变换 | 仿射变换 | ||||
---|---|---|---|---|---|---|
亮度 | 对比度 | 水平翻转 | 缩放 | 旋转 | 平移 | |
0 | 0.0 | 0.0 | 0.5 | 0.0 | 0 | 0.10 |
1 | 0.1 | 0.1 | 0.5 | 0.3 | 15 | 0.05 |
2 | 0.2 | 0.2 | 0.5 | 0.3 | 25 | 0.10 |
3 | 0.3 | 0.3 | 0.5 | 0.3 | 35 | 0.15 |
4 | 0.4 | 0.4 | 0.5 | 0.3 | 45 | 0.20 |
Tab. 2 Five augmentation levels of MRAugment
等级 | 颜色变换 | 仿射变换 | ||||
---|---|---|---|---|---|---|
亮度 | 对比度 | 水平翻转 | 缩放 | 旋转 | 平移 | |
0 | 0.0 | 0.0 | 0.5 | 0.0 | 0 | 0.10 |
1 | 0.1 | 0.1 | 0.5 | 0.3 | 15 | 0.05 |
2 | 0.2 | 0.2 | 0.5 | 0.3 | 25 | 0.10 |
3 | 0.3 | 0.3 | 0.5 | 0.3 | 35 | 0.15 |
4 | 0.4 | 0.4 | 0.5 | 0.3 | 45 | 0.20 |
方法 | MRAugment等级/等级组合 | 验证集AUC | 测试集AUC | ||||
---|---|---|---|---|---|---|---|
平均值 | 标准差 | 中位数 | 平均值 | 标准差 | 中位数 | ||
MRSL | 0.936 | 0.008 | 0.938 | 0.856 | 0.012 | 0.858 | |
0.956 | 0.010 | 0.960 | 0.916 | 0.012 | 0.914 | ||
0.965 | 0.009 | 0.965 | 0.919 | 0.013 | 0.915 | ||
0.961 | 0.013 | 0.964 | 0.906 | 0.013 | 0.909 | ||
MRSSL | 0.947 | 0.010 | 0.946 | 0.928 | 0.012 | 0.926 | |
0.954 | 0.009 | 0.957 | 0.939 | 0.006 | 0.939 | ||
0.960 | 0.008 | 0.960 | 0.946 | 0.010 | 0.949 | ||
0.951 | 0.005 | 0.952 | 0.944 | 0.015 | 0.944 | ||
0.951 | 0.011 | 0.950 | 0.942 | 0.009 | 0.945 | ||
0.951 | 0.011 | 0.952 | 0.942 | 0.008 | 0.941 | ||
0.949 | 0.007 | 0.947 | 0.933 | 0.010 | 0.931 |
Tab. 3 AUC statistics of MRSL with different MRAugment augmentation levels and MRSSL with different MRAugment level combinations
方法 | MRAugment等级/等级组合 | 验证集AUC | 测试集AUC | ||||
---|---|---|---|---|---|---|---|
平均值 | 标准差 | 中位数 | 平均值 | 标准差 | 中位数 | ||
MRSL | 0.936 | 0.008 | 0.938 | 0.856 | 0.012 | 0.858 | |
0.956 | 0.010 | 0.960 | 0.916 | 0.012 | 0.914 | ||
0.965 | 0.009 | 0.965 | 0.919 | 0.013 | 0.915 | ||
0.961 | 0.013 | 0.964 | 0.906 | 0.013 | 0.909 | ||
MRSSL | 0.947 | 0.010 | 0.946 | 0.928 | 0.012 | 0.926 | |
0.954 | 0.009 | 0.957 | 0.939 | 0.006 | 0.939 | ||
0.960 | 0.008 | 0.960 | 0.946 | 0.010 | 0.949 | ||
0.951 | 0.005 | 0.952 | 0.944 | 0.015 | 0.944 | ||
0.951 | 0.011 | 0.950 | 0.942 | 0.009 | 0.945 | ||
0.951 | 0.011 | 0.952 | 0.942 | 0.008 | 0.941 | ||
0.949 | 0.007 | 0.947 | 0.933 | 0.010 | 0.931 |
方法 | AUC | 准确率 | 敏感性 | 特异性 | G-mean |
---|---|---|---|---|---|
两者差值 | 0.043 | 0.025 | -0.009 | 0.160 | 0.085 |
MRSL | 0.908 [0.838,0.978] | 0.858 [0.785,0.910] | 0.916 [0.843,0.957] | 0.640 [0.445,0.798] | 0.766 |
MRSSL | 0.951 [0.913,0.989] | 0.883 [0.814,0.929] | 0.905 [0.830,0.949] | 0.800 [0.609,0.911] | 0.851 |
Tab. 4 Result comparison between the selected models of MRSL and MRSSL on 5 evaluation indicators
方法 | AUC | 准确率 | 敏感性 | 特异性 | G-mean |
---|---|---|---|---|---|
两者差值 | 0.043 | 0.025 | -0.009 | 0.160 | 0.085 |
MRSL | 0.908 [0.838,0.978] | 0.858 [0.785,0.910] | 0.916 [0.843,0.957] | 0.640 [0.445,0.798] | 0.766 |
MRSSL | 0.951 [0.913,0.989] | 0.883 [0.814,0.929] | 0.905 [0.830,0.949] | 0.800 [0.609,0.911] | 0.851 |
方法 | AUC | 准确率 | 敏感性 | 特异性 | G-mean |
---|---|---|---|---|---|
MRSL(数据扩增) | 0.919±0.013 | 0.876±0.009 | 0.716±0.047 | 0.810±0.026 | |
MRSL(高斯噪声) | 0.887±0.019 | 0.849±0.022 | 0.905±0.029 | 0.636±0.081 | 0.757±0.046 |
PI Model | 0.890±0.023 | 0.844±0.018 | 0.882±0.034 | 0.698±0.133 | 0.780±0.062 |
FixMatch | 0.930±0.011 | 0.875±0.021 | 0.901±0.035 | 0.778±0.078 | |
UDA | 0.916±0.024 | 0.756±0.061 | 0.831±0.030 | ||
MRSSL | 0.946±0.010 | 0.893±0.013 | 0.925±0.019 | 0.843±0.025 |
Tab. 5 Result comparison of different methods
方法 | AUC | 准确率 | 敏感性 | 特异性 | G-mean |
---|---|---|---|---|---|
MRSL(数据扩增) | 0.919±0.013 | 0.876±0.009 | 0.716±0.047 | 0.810±0.026 | |
MRSL(高斯噪声) | 0.887±0.019 | 0.849±0.022 | 0.905±0.029 | 0.636±0.081 | 0.757±0.046 |
PI Model | 0.890±0.023 | 0.844±0.018 | 0.882±0.034 | 0.698±0.133 | 0.780±0.062 |
FixMatch | 0.930±0.011 | 0.875±0.021 | 0.901±0.035 | 0.778±0.078 | |
UDA | 0.916±0.024 | 0.756±0.061 | 0.831±0.030 | ||
MRSSL | 0.946±0.010 | 0.893±0.013 | 0.925±0.019 | 0.843±0.025 |
1 | LITJENS G, KOOI T, EHTESHAMI BEJNORDI B, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42:60-88. 10.1016/j.media.2017.07.005 |
2 | SHEN D G, WU G R, SUK H I. Deep learning in medical image analysis[J]. Annual Review of Biomedical Engineering, 2017, 19: 221-248. 10.1146/annurev-bioeng-071516-044442 |
3 | SONG Y, ZHANG Y D, YAN X, et al. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI[J]. Journal of Magnetic Resonance Imaging, 2018, 48(6): 1570-1577. 10.1002/jmri.26047 |
4 | HERENT P, SCHMAUCH B, JEHANNO P, et al. Detection and characterization of MRI breast lesions using deep learning[J]. Diagnostic and Interventional Imaging, 2019, 100(4): 219-225. 10.1016/j.diii.2019.02.008 |
5 | ZHOU Z Y, ZHAO G Y, KIJOWSKI R, et al. Deep convolutional neural network for segmentation of knee joint anatomy[J]. Magnetic Resonance in Medicine, 2018, 80(6): 2759-2770. 10.1002/mrm.27229 |
6 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. 10.1109/cvpr.2016.90 |
7 | HE K M, ZHANG X Y, REN S Q, et al. Identity mappings in deep residual networks[C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS9908. Cham: Springer, 2016: 630-645. |
8 | ZAGORUYKO S, KOMODAKIS N. Wide residual networks[C]// Proceedings of the 2016 British Machine Vision Conference. Durham: BMVA Press, 2016: No.87. 10.5244/c.30.87 |
9 | PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359. 10.1109/tkde.2009.191 |
10 | BIEN N, RAJPURKAR P, BALL RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet[J]. PLoS Medicine, 2018, 15(11): No.e1002699. 10.1371/journal.pmed.1002699 |
11 | DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 248-255. 10.1109/cvpr.2009.5206848 |
12 | ZHU X J, GOLDBERG A B. Introduction to Semi-Supervised Learning[M]. San Rafael, CA: Morgan & Claypool Publishers, 2009: 23-33. 10.3115/1621829.1621832 |
13 | LAINE S, AILA T. Temporal ensembling for semi-supervised learning[EB/OL]. (2017-03-15) [2020-12-12].. |
14 | MIYATO T, MAEDA S I, KOYAMA M, et al. Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(8): 1979-1993. 10.1109/tpami.2018.2858821 |
15 | TARVAINEN A, VALPOLA H. Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., 2017: 1195-1204. |
16 | XIE Q Z, DAI Z H, HOVY E, et al. Unsupervised data augmentation for consistency training[C/OL]// Proceedings of the 34th International Conference on Neural Information Processing Systems. [2020-12-12].. |
17 | CHEPLYGINA V, DE BRUIJNE M, PLUIM J P W. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis[J]. Medical Image Analysis, 2019, 54: 280-296. 10.1016/j.media.2019.03.009 |
18 | LIU Q D, YU L Q, LUO L Y, et al. Semi-supervised medical image classification with relation-driven self-ensembling model[J]. IEEE Transactions on Medical Imaging, 2020, 39(11): 3429-3440. 10.1109/tmi.2020.2995518 |
19 | NGUYEN H H, SAARAKKALA S, BLASCHKO M B, et al. Semixup: in- and out-of-manifold regularization for deep semi-supervised knee osteoarthritis severity grading from plain radiographs[J]. IEEE Transactions on Medical Imaging, 2020, 39: 4346-4356. 10.1109/tmi.2020.3017007 |
20 | PETERFY C G, SCHNEIDER E, NEVITT M. The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee[J]. Osteoarthritis and Cartilage, 2008, 16(12): 1433-1441. 10.1016/j.joca.2008.06.016 |
21 | HU J L, KUANG Y Z, LIAO B, et al. A multichannel 2D convolutional neural network model for task-evoked fMRI data classification[J]. Computational Intelligence and Neuroscience, 2019, 2019: No.5065214. 10.1155/2019/5065214 |
22 | MITCHELL T M. The need for biases in learning generalizations: Rutgers CS tech report CBM-TR-117[R]. New Brunswick, NJ: Rutgers University, 1980: 1-3. |
23 | CUBUK E D, ZOPH B, SHLENS J, et al. RandAugment: practical automated data augmentation with a reduced search space[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2020: 3008-3017. 10.1109/cvprw50498.2020.00359 |
24 | NYÚL L G, UDUPA J K. On standardizing the MR image intensity scale[J]. Magnetic Resonance in Medicine, 1999, 42(6): 1072-1081. 10.1002/(sici)1522-2594(199912)42:6<1072::aid-mrm11>3.0.co;2-m |
25 | LOWEKAMP B C, CHEN D T, IBÁÑEZ L, et al. The design of simpleITK[J]. Frontiers in Neuroinformatics, 2013, 7: No.45. 10.3389/fninf.2013.00045 |
26 | YANIV Z, LOWEKAMP B C, JOHNSON H J, et al. SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research[J]. Journal of Digital Imaging, 2018, 31(3): 290-303. 10.1007/s10278-017-0037-8 |
27 | WALT S VAN DER, SCHÖNBERGER J L, NUNEZ-IGLESIAS J, et al. Scikit-image: image processing in Python[J]. PeerJ, 2014, 2: No.e453. 10.7717/peerj.453 |
28 | JAPKOWICZ N, STEPHEN S. The class imbalance problem: a systematic study[J]. Intelligent Data Analysis, 2002, 6(5): 429-449. 10.3233/ida-2002-6504 |
29 | SOHN K, BERTHELOT D, LI C L, et al. FixMatch: simplifying semi-supervised learning with consistency and confidence[C/OL]// Proceedings of the 34th International Conference on Neural Information Processing Systems. [2020-12-12].. |
30 | PASZKE A, GROSS S, MASSA F, et al. PyTorch: an imperative style, high-performance deep learning library[C/OL]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. [2020-12-12].. |
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