# Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

arXiv: https://arxiv.org/abs/1805.07549 · HTML: https://mlsanity.com/paper/1805.07549/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2018.

## Built on by (most cited first, 30 of 39)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022)
- [Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization](https://mlsanity.com/paper/2306.09264/) (2023)
- [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021)
- [Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition](https://mlsanity.com/paper/2104.11057/) (2021)
- [ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images](https://mlsanity.com/paper/2202.07983/) (2022)
- [Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition](https://mlsanity.com/paper/2111.08913/) (2021)
- [Deep learning on fundus images detects glaucoma beyond the optic disc](https://mlsanity.com/paper/2103.11895/) (2021)
- [SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer](https://mlsanity.com/paper/2206.05763/) (2022)
- [Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset](https://mlsanity.com/paper/2207.07918/) (2022)
- [Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network](https://mlsanity.com/paper/2410.14965/) (2024)
- [CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2110.02417/) (2021)
- [Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations](https://mlsanity.com/paper/2301.11315/) (2023)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models](https://mlsanity.com/paper/2503.02917/) (2025)
- [Opinions Vary? Diagnosis First!](https://mlsanity.com/paper/2202.06505/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.16573/) (2024)
- [GONet: A Generalizable Deep Learning Model for Glaucoma Detection](https://mlsanity.com/paper/2502.19514/) (2025)
- [I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications](https://mlsanity.com/paper/2104.02609/) (2021)
- [FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation](https://mlsanity.com/paper/2503.17831/) (2025)
- [U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina](https://mlsanity.com/paper/2107.04721/) (2021)
- [Label uncertainty-guided multi-stream model for disease screening](https://mlsanity.com/paper/2201.12089/) (2022)
- [ExpNet: A unified network for Expert-Level Classification](https://mlsanity.com/paper/2211.15672/) (2022)
- [Soft Dice Confidence: A Near-Optimal Confidence Estimator for Selective Prediction in Semantic Segmentation](https://mlsanity.com/paper/2402.10665/) (2024)
- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [Rapid Classification of Glaucomatous Fundus Images](https://mlsanity.com/paper/2102.04400/) (2021)
- [Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images](https://mlsanity.com/paper/2105.03068/) (2021)
- [Learning to screen Glaucoma like the ophthalmologists](https://mlsanity.com/paper/2209.11431/) (2022)
- [EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc](https://mlsanity.com/paper/2308.10192/) (2023)

## Abstract (arXiv)

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods firstly segment the main structure, and subsequently calculate the clinical measurement for detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy, and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information, and screen glaucoma from the fundus image directly. Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are respectively considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma datasets (SCES and new SINDI datasets) show our method outperforms other state-of-the-art algorithms.