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

arXiv: https://arxiv.org/abs/2103.03451 · HTML: https://mlsanity.com/paper/2103.03451/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [LadderNet: Multi-path networks based on U-Net for medical image segmentation](https://mlsanity.com/paper/1810.07810/) (2018)
- [An Elastic Interaction-Based Loss Function for Medical Image Segmentation](https://mlsanity.com/paper/2007.02663/) (2020)
- [Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network](https://mlsanity.com/paper/2004.12776/) (2020)
- [Retinal Image Segmentation with a Structure-Texture Demixing Network](https://mlsanity.com/paper/2008.00817/) (2020)

## Built on by (most cited first, 20 of 20)

- [HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance](https://mlsanity.com/paper/2307.07829/) (2023)
- [DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences](https://mlsanity.com/paper/2306.12153/) (2023)
- [NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment](https://mlsanity.com/paper/2505.16314/) (2025)
- [RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation](https://mlsanity.com/paper/2309.15638/) (2023)
- [TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2411.01403/) (2024)
- [Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation](https://mlsanity.com/paper/2208.06283/) (2022)
- [SCOPE: Structural Continuity Preservation for Medical Image Segmentation](https://mlsanity.com/paper/2304.14572/) (2023)
- [FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures](https://mlsanity.com/paper/2311.08059/) (2023)
- [Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization](https://mlsanity.com/paper/2406.14994/) (2024)
- [Attention W-Net: Improved Skip Connections for better Representations](https://mlsanity.com/paper/2110.08811/) (2021)
- [Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation](https://mlsanity.com/paper/2412.18089/) (2024)
- [Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends](https://mlsanity.com/paper/2501.04073/) (2025)
- [Logarithmic Mathematical Morphology: Theory and applications](https://mlsanity.com/paper/2309.02007/) (2023)
- [A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features](https://mlsanity.com/paper/2405.16235/) (2024)
- [Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures](https://mlsanity.com/paper/2412.20608/) (2024)
- [VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement](https://mlsanity.com/paper/2511.18763/) (2025)
- [Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy](https://mlsanity.com/paper/2603.09932/) (2026)
- [Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement](https://mlsanity.com/paper/2604.03806/) (2026)
- [Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency](https://mlsanity.com/paper/2607.05965/) (2026)
- [Observer Choice and Threshold Selection in Retinal Vessel Segmentation: A Subject-Separated Evaluation](https://mlsanity.com/paper/2609.25597/) (2026)

## Abstract (arXiv)

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.