Papers › Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
Yuqian Zhou, Hanchao Yu, Humphrey Shi
Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-performance deep learning-based approaches to segment the retinal images in an end-to-end manner. However, the acquisition of retinal vessel images and segmentation labels requires onerous work from professional clinicians, which results in smaller training dataset with incomplete labels. As known, data-driven methods suffer from data insufficiency, and the models will easily over-fit the small-scale training data. Such a situation becomes more severe when the training vessel labels are incomplete or incorrect. In this paper, we propose a Study Group Learning (SGL) scheme to improve the robustness of the model trained on noisy labels. Besides, a learned enhancement map provides better visualization than conventional methods as an auxiliary tool for clinicians. Experiments demonstrate that the proposed method further improves the vessel segmentation performance in DRIVE and CHASE_DB1 datasets, especially when the training labels are noisy.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | AUC | 0.9920 | #2 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | F1 score | 0.8271 | #2 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | Sensitivity | 0.8690 | #2 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | Study Group Learning | AUC | 0.9886 | #3 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | Study Group Learning | F1 score | 0.8316 | #3 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | Study Group Learning | sensitivity | 0.8380 | #3 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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