# Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

arXiv: https://arxiv.org/abs/1706.04372 · HTML: https://mlsanity.com/paper/1706.04372/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 28 of 28)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images](https://mlsanity.com/paper/2107.08274/) (2021)
- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [Efficient Classification of Very Large Images with Tiny Objects](https://mlsanity.com/paper/2106.02694/) (2021)
- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (2022)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation](https://mlsanity.com/paper/2110.14160/) (2021)
- [FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing](https://mlsanity.com/paper/2203.16168/) (2022)
- [SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2210.10969/) (2022)
- [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images](https://mlsanity.com/paper/2211.14552/) (2022)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading](https://mlsanity.com/paper/2108.06763/) (2021)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images](https://mlsanity.com/paper/2103.01702/) (2021)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy](https://mlsanity.com/paper/2103.04207/) (2021)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning](https://mlsanity.com/paper/2204.00624/) (2022)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Towards Accurate Acne Detection via Decoupled Sequential Detection Head](https://mlsanity.com/paper/2301.12219/) (2023)
- [A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images](https://mlsanity.com/paper/2404.04887/) (2024)
- [CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound](https://mlsanity.com/paper/2206.08524/) (2022)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Diabetic Retinopathy Detection Using Quantum Transfer Learning](https://mlsanity.com/paper/2405.01734/) (2024)
- [Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.19362/) (2025)
- [Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection](https://mlsanity.com/paper/2509.09844/) (2025)
- [Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis](https://mlsanity.com/paper/2602.03622/) (2026)
- [HiResNets: Native Full-HD Video Recognition with Foveal Residual Streams](https://mlsanity.com/paper/2608.02140/) (2026)

## Abstract (arXiv)

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoomin-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. 2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.