# ELF: An End-to-end Local and Global Multimodal Fusion Framework for Glaucoma Grading

arXiv: https://arxiv.org/abs/2311.08032 · HTML: https://mlsanity.com/paper/2311.08032/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022)
- [COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading](https://mlsanity.com/paper/2201.03795/) (2022)
- [Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks](https://mlsanity.com/paper/2106.13551/) (2021)

## Built on by (most cited first, 3 of 3)

- [Efficient Adversarial Training via Criticality-Aware Fine-Tuning](https://mlsanity.com/paper/2604.12780/) (2026)
- [GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification](https://mlsanity.com/paper/2603.12800/) (2026)
- [Text-Anchored Semantic Perturbations for Transferable Jailbreak Attacks on Multimodal Large Language Models](https://mlsanity.com/paper/2608.22312/) (2026)

## Abstract (arXiv)

Glaucoma is a chronic neurodegenerative condition that can lead to blindness. Early detection and curing are very important in stopping the disease from getting worse for glaucoma patients. The 2D fundus images and optical coherence tomography(OCT) are useful for ophthalmologists in diagnosing glaucoma. There are many methods based on the fundus images or 3D OCT volumes; however, the mining for multi-modality, including both fundus images and data, is less studied. In this work, we propose an end-to-end local and global multi-modal fusion framework for glaucoma grading, named ELF for short. ELF can fully utilize the complementary information between fundus and OCT. In addition, unlike previous methods that concatenate the multi-modal features together, which lack exploring the mutual information between different modalities, ELF can take advantage of local-wise and global-wise mutual information. The extensive experiment conducted on the multi-modal glaucoma grading GAMMA dataset can prove the effiectness of ELF when compared with other state-of-the-art methods.