# Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

arXiv: https://arxiv.org/abs/1706.09634 · HTML: https://mlsanity.com/paper/1706.09634/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 18 of 18)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges](https://mlsanity.com/paper/2303.14133/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition](https://mlsanity.com/paper/2111.08913/) (2021)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation](https://mlsanity.com/paper/2205.15720/) (2022)
- [Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound](https://mlsanity.com/paper/2503.20685/) (2025)
- [Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy](https://mlsanity.com/paper/2103.04207/) (2021)
- [Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training](https://mlsanity.com/paper/2202.06104/) (2022)
- [OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation](https://mlsanity.com/paper/2207.12238/) (2022)
- [Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning](https://mlsanity.com/paper/2212.00565/) (2022)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection](https://mlsanity.com/paper/2211.03148/) (2022)
- [Diabetic Retinopathy Detection Using Quantum Transfer Learning](https://mlsanity.com/paper/2405.01734/) (2024)
- [Lesion-aware network for diabetic retinopathy diagnosis](https://mlsanity.com/paper/2408.07264/) (2024)
- [TWLR: Text-Guided Weakly-Supervised Lesion Localization and Severity Regression for Explainable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2512.13008/) (2025)
- [Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution](https://mlsanity.com/paper/2605.05283/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.