# Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation

arXiv: https://arxiv.org/abs/2503.05319 · HTML: https://mlsanity.com/paper/2503.05319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [SuS-X: Training-Free Name-Only Transfer of Vision-Language Models](https://mlsanity.com/paper/2211.16198/) (2022)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2310.01035/) (2023)
- [Metric Learning with Adaptive Density Discrimination](https://mlsanity.com/paper/1511.05939/) (2015)
- [Decoupling Common and Unique Representations for Multimodal Self-supervised Learning](https://mlsanity.com/paper/2309.05300/) (2023)
- [Computational Pathology: A Survey Review and The Way Forward](https://mlsanity.com/paper/2304.05482/) (2023)
- [ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition](https://mlsanity.com/paper/2110.07703/) (2021)
- [Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis](https://mlsanity.com/paper/2502.11724/) (2025)
- [Confidence-aware multi-modality learning for eye disease screening](https://mlsanity.com/paper/2405.18167/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction](https://mlsanity.com/paper/2607.09982/) (2026)

## Abstract (arXiv)

This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. However, the paper highlights two key limitations of these approaches: (i) Task-irrelevant redundant information (e.g., numerous slices) in complex modalities leads to significant redundancy in latent space representations. (ii) Overlapping multimodal representations make it difficult to extract unique features for each modality. To overcome these challenges, the authors propose the Essence-Point and Disentangle Representation Learning (EDRL) strategy, which integrates a self-distillation mechanism into an end-to-end framework to enhance feature selection and disentanglement for more robust multimodal learning. Specifically, the Essence-Point Representation Learning module selects discriminative features that improve disease grading performance. The Disentangled Representation Learning module separates multimodal data into modality-common and modality-unique representations, reducing feature entanglement and enhancing both robustness and interpretability in ophthalmic disease diagnosis. Experiments on multimodal ophthalmology datasets show that the proposed EDRL strategy significantly outperforms current state-of-the-art methods.