# Deep image mining for diabetic retinopathy screening

arXiv: https://arxiv.org/abs/1610.07086 · HTML: https://mlsanity.com/paper/1610.07086/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2016.

## Built on by (most cited first, 25 of 25)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Certifiably Robust Interpretation via Renyi Differential Privacy](https://mlsanity.com/paper/2107.01561/) (2021)
- [Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation](https://mlsanity.com/paper/2110.14160/) (2021)
- [Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation](https://mlsanity.com/paper/2210.05946/) (2022)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images](https://mlsanity.com/paper/2103.01702/) (2021)
- [Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy](https://mlsanity.com/paper/2211.11509/) (2022)
- [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)
- [DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2401.05137/) (2024)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina](https://mlsanity.com/paper/2108.02798/) (2021)
- [Diabetic Retinopathy Lesion Segmentation through Attention Mechanisms](https://mlsanity.com/paper/2602.07301/) (2026)
- [Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization](https://mlsanity.com/paper/2203.13856/) (2022)
- [Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection](https://mlsanity.com/paper/2208.02408/) (2022)
- [Detection of diabetic retinopathy using longitudinal self-supervised learning](https://mlsanity.com/paper/2209.00915/) (2022)
- [A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images](https://mlsanity.com/paper/2210.03180/) (2022)
- [Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2110.03877/) (2021)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection](https://mlsanity.com/paper/2304.12719/) (2023)
- [Introducing Feature Attention Module on Convolutional Neural Network for Diabetic Retinopathy Detection](https://mlsanity.com/paper/2308.02985/) (2023)
- [Diabetic Retinopathy Detection Using Quantum Transfer Learning](https://mlsanity.com/paper/2405.01734/) (2024)
- [FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment](https://mlsanity.com/paper/2506.20303/) (2025)
- [Robust Multi-Disease Retinal Classification via Xception-Based Transfer Learning and W-Net Vessel Segmentation](https://mlsanity.com/paper/2512.10608/) (2025)
- [CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability](https://mlsanity.com/paper/2604.12305/) (2026)
- [An Event-Driven Framework for Fly-Inspired Visual Motion Detection](https://mlsanity.com/paper/2607.05205/) (2026)

## Abstract (arXiv)

Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: Az = 0.954 in Kaggle's dataset and Az = 0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. The proposed detector outperforms recent algorithms trained to detect those lesions specifically, as well as competing heatmap generation algorithms for ConvNets. This detector is part of the Messidor system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images.