Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

5 Mar 2021  ·  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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Retinal Vessel Segmentation CHASE_DB1 Study Group Learning F1 score 0.8271 # 2
AUC 0.9920 # 1
Sensitivity 0.8690 # 2
Retinal Vessel Segmentation DRIVE Study Group Learning F1 score 0.8316 # 2
AUC 0.9886 # 3
sensitivity 0.8380 # 1


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