ArticleProceedings of SPIE--the International Society for Optical Engineering2022
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification.Journal of imaging informatics in medicine · 2025Article
- RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- nnMobileNet: Rethinking CNN for Retinopathy Research.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.Computers in biology and medicine · 2023Article
- OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing.Information processing in medical imaging : proceedings of the ... conference · 2023Article
- OPTIMAL TRANSPORT GUIDED UNSUPERVISED LEARNING FOR ENHANCING LOW-QUALITY RETINAL IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motivates us to develop algorithms to classify rDR and segment lesions via image-level labels. This paper leverages self-supervised equivariant learning and attention-based multi-instance learning (MIL) to tackle this problem. MIL is an effective strategy to differentiate positive and negative instances, helping us discard background regions (negative instances) while localizing lesion regions (positive ones). However, MIL only provides coarse lesion localization and cannot distinguish lesions located across adjacent patches. Conversely, a self-supervised equivariant attention mechanism (SEAM) generates a segmentation-level class activation map (CAM) that can guide patch extraction of lesions more accurately. Our work aims at integrating both methods to improve rDR classification accuracy. We conduct extensive validation experiments on the Eyepacs dataset, achieving an area under the receiver operating characteristic curve (AU ROC) of 0.958, outperforming current state-of-the-art algorithms.
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