# Learn to Segment Retinal Lesions and Beyond

arXiv: https://arxiv.org/abs/1912.11619 · HTML: https://mlsanity.com/paper/1912.11619/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2019.

## Built on by (most cited first, 30 of 33)

- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching](https://mlsanity.com/paper/2207.07932/) (2022)
- [Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains](https://mlsanity.com/paper/2307.04378/) (2023)
- [Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt](https://mlsanity.com/paper/2505.09264/) (2025)
- [Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion](https://mlsanity.com/paper/2309.09306/) (2023)
- [FedBone: Towards Large-Scale Federated Multi-Task Learning](https://mlsanity.com/paper/2306.17465/) (2023)
- [Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses](https://mlsanity.com/paper/2104.08717/) (2021)
- [Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations](https://mlsanity.com/paper/2406.06384/) (2024)
- [ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model](https://mlsanity.com/paper/2408.10894/) (2024)
- [INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning](https://mlsanity.com/paper/2506.03660/) (2025)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [FunBench: Benchmarking Fundus Reading Skills of MLLMs](https://mlsanity.com/paper/2503.00901/) (2025)
- [GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy](https://mlsanity.com/paper/2311.08075/) (2023)
- [Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning](https://mlsanity.com/paper/2110.09319/) (2021)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Image Forgery Localization with State Space Models](https://mlsanity.com/paper/2412.11214/) (2024)
- [In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging](https://mlsanity.com/paper/2508.13880/) (2025)
- [Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images](https://mlsanity.com/paper/2305.08078/) (2023)
- [Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence](https://mlsanity.com/paper/2605.22414/) (2026)
- [Coarse Retinal Lesion Annotations Refinement via Prototypical Learning](https://mlsanity.com/paper/2208.14294/) (2022)
- [RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images](https://mlsanity.com/paper/2403.15647/) (2024)
- [Cross-Dataset Generalization For Retinal Lesions Segmentation](https://mlsanity.com/paper/2405.08329/) (2024)
- [Multi-style conversion for semantic segmentation of lesions in fundus images by adversarial attacks](https://mlsanity.com/paper/2410.13822/) (2024)
- [Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy Grading](https://mlsanity.com/paper/2411.02614/) (2024)
- [Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization](https://mlsanity.com/paper/2502.05593/) (2025)
- [Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.19362/) (2025)
- [CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging](https://mlsanity.com/paper/2509.08618/) (2025)
- [Explainable Fundus Image Curation and Lesion Detection in Diabetic Retinopathy](https://mlsanity.com/paper/2512.08986/) (2025)
- [Human Knowledge Integrated Multi-modal Learning for Single Source Domain Generalization](https://mlsanity.com/paper/2603.12369/) (2026)

## Abstract (arXiv)

Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path re-designed to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side-attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large-scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading