  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.
