The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
Abstract
Medical image analysis has two important research areas: disease grading and fine-grained lesion segmentation. Although the former problem often relies on the latter, the two are usually studied separately. Disease severity grading can be treated as a classification problem, which only requires image-level annotations, while the lesion segmentation requires stronger pixel-level annotations. However, pixel-wise data annotation for medical images is highly time-consuming and requires domain experts. In this paper, we propose a collaborative learning method to jointly improve the performance of disease grading and lesion segmentation by semi-supervised learning with an attention mechanism. Given a small set of pixel-level annotated data, a multi-lesion mask generation model first performs the traditional semantic segmentation task. Then, based on initially predicted lesion maps for large quantities of image-level annotated data, a lesion attentive disease grading model is designed to improve the severity classification accuracy. Meanwhile, the lesion attention model can refine the lesion maps using class-specific information to fine-tune the segmentation model in a semi-supervised manner. An adversarial architecture is also integrated for training. With extensive experiments on a representative medical problem called diabetic retinopathy (DR), we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art methods on three public datasets.
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Cites
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Focal Loss for Dense Object Detection
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AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
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Self-citationYan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.036501 · Cited by 573
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Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Self-citationAssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation
Coupé, P., Mansencal, B., Clément, M., Giraud, R., Denis de Senneville, B., Ta, V.-T., Lepetit, V., & Manjon, J. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117026 · Cited by 206
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection
Wang, D., Zhang, Y., Zhang, K., & Wang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00401 · Cited by 157
Zhou, Y., Wang, B., He, X., Cui, S., & Shao, L. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3045475 · Cited by 155
Self-citationRotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis
Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P.-A., & Xing, L. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3075244 · Cited by 105
Metadata
- Authors
- Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Volume
- 2019
- Pages
- 2074-2083
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2978017498
- Cited by
- 285
- References
- 9
- FWCI
- 51.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2019). Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00218
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