# Guided Context Gating: Learning to leverage salient lesions in retinal fundus images

arXiv: https://arxiv.org/abs/2406.13126 · HTML: https://mlsanity.com/paper/2406.13126/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://mlsanity.com/paper/2004.01461/) (2020)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Global Context Networks](https://mlsanity.com/paper/2012.13375/) (2020)

## Built on by (most cited first, 7 of 7)

- [Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading](https://mlsanity.com/paper/2509.26146/) (2025)
- [Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis](https://mlsanity.com/paper/2504.20306/) (2025)
- [GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning](https://mlsanity.com/paper/2412.17251/) (2024)
- [DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation](https://mlsanity.com/paper/2604.17209/) (2026)
- [Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging](https://mlsanity.com/paper/2604.17222/) (2026)
- [Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing](https://mlsanity.com/paper/2608.09752/) (2026)
- [C2A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2608.09774/) (2026)

## Abstract (arXiv)

Effectively representing medical images, especially retinal images, presents a considerable challenge due to variations in appearance, size, and contextual information of pathological signs called lesions. Precise discrimination of these lesions is crucial for diagnosing vision-threatening issues such as diabetic retinopathy. While visual attention-based neural networks have been introduced to learn spatial context and channel correlations from retinal images, they often fall short in capturing localized lesion context. Addressing this limitation, we propose a novel attention mechanism called Guided Context Gating, an unique approach that integrates Context Formulation, Channel Correlation, and Guided Gating to learn global context, spatial correlations, and localized lesion context. Our qualitative evaluation against existing attention mechanisms emphasize the superiority of Guided Context Gating in terms of explainability. Notably, experiments on the Zenodo-DR-7 dataset reveal a substantial 2.63% accuracy boost over advanced attention mechanisms & an impressive 6.53% improvement over the state-of-the-art Vision Transformer for assessing the severity grade of retinopathy, even with imbalanced and limited training samples for each class.