Weakly Supervised Patch Annotation for Improved Screening of Diabetic Retinopathy

2026-03-04

AI Summary

This study addresses the challenges of sparse lesion annotations and high expert labeling costs in early diabetic retinopathy (DR) screening, which hinder the performance of deep learning models. To overcome these limitations, the authors propose the SAFE framework, a two-stage weakly supervised learning approach that integrates a dual-branch patch embedding network, contrastive learning, and multi-embedding space ensembling. By leveraging spatial-semantic proximity to propagate sparse annotations and incorporating an abstention mechanism to ensure label reliability, SAFE achieves a patch-level classification accuracy of 0.9886 between healthy and diseased regions. The method significantly improves downstream DR classification performance, attaining an F1 score and AUPRC of 0.545, and is validated by ophthalmologists to focus on clinically relevant lesions, demonstrating both high interpretability and practical utility.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

Diabetic Retinopathy (DR) requires timely screening to prevent irreversible vision loss. However, its early detection remains a significant challenge since often the subtle pathological manifestations (lesions) get overlooked due to insufficient annotation. Existing literature primarily focuses on image-level supervision, weakly-supervised localization, and clustering-based representation learning, which fail to systematically annotate unlabeled lesion region(s) for refining the dataset. Expert-driven lesion annotation is labor-intensive and often incomplete, limiting the performance of deep learning models. We introduce Similarity-based Annotation via Feature-space Ensemble (SAFE), a two-stage framework that unifies weak supervision, contrastive learning, and patch-wise embedding inference, to systematically expand sparse annotations in the pathology. SAFE preserves fine-grained details of the lesion(s) under partial clinical supervision. In the first stage, a dual-arm Patch Embedding Network learns semantically structured, class-discriminative embeddings from expert annotated patches. Next, an ensemble of independent embedding spaces extrapolates labels to the unannotated regions based on spatial and semantic proximity. An abstention mechanism ensures trade-off between highly reliable annotation and noisy coverage. Experimental results demonstrate reliable separation of healthy and diseased patches, achieving upto 0.9886 accuracy. The annotation generated from SAFE substantially improves downstream tasks such as DR classification, demonstrating a substantial increase in F1-score of the diseased class and a performance gain as high as 0.545 in Area Under the Precision-Recall Curve (AUPRC). Qualitative analysis, with explainability, confirms that SAFE focuses on clinically relevant lesion patterns; and is further validated by ophthalmologists.

Problem

• Diabetic Retinopathy
• Weak Supervision
• Lesion Annotation
• Patch-level Labeling
• Screening

Innovation

• weakly supervised learning
• contrastive learning
• patch-level annotation
• feature-space ensemble
• diabetic retinopathy screening