# Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images

arXiv: https://arxiv.org/abs/2308.00291 · HTML: https://mlsanity.com/paper/2308.00291/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [Channel-wise Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/2011.13256/) (2020)
- [Knowledge Distillation with the Reused Teacher Classifier](https://mlsanity.com/paper/2203.14001/) (2022)
- [Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization](https://mlsanity.com/paper/2012.01879/) (2020)
- [Multi-Modal Multi-Instance Learning for Retinal Disease Recognition](https://mlsanity.com/paper/2109.12307/) (2021)
- [Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization](https://mlsanity.com/paper/1907.12023/) (2019)
- [Multimodal Information Fusion for Glaucoma and DR Classification](https://mlsanity.com/paper/2209.00979/) (2022)

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

- [MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images](https://mlsanity.com/paper/2412.09402/) (2024)
- [An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis](https://mlsanity.com/paper/2407.03018/) (2024)
- [CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis](https://mlsanity.com/paper/2407.17620/) (2024)
- [PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis](https://mlsanity.com/paper/2503.04836/) (2025)
- [KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification](https://mlsanity.com/paper/2512.09069/) (2025)
- [Clinical Graph-Mediated Distillation for Unpaired MRI-to-CFI Hypertension Prediction](https://mlsanity.com/paper/2603.21809/) (2026)
- [Shared Semantic Codebook Distillation for Unpaired Cross-Modal Medical Classification](https://mlsanity.com/paper/2607.27357/) (2026)

## Abstract (arXiv)

Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examination. Since collecting OCT images is relatively more expensive than fundus photographs, existing methods use multi-modal learning to complement limited OCT data with additional context from fundus images. However, the multi-modal framework requires eye-paired datasets of both modalities, which is impractical for clinical use. To address this problem, we propose a novel fundus-enhanced disease-aware distillation model (FDDM), for retinal disease classification from OCT images. Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use. Specifically, we propose a novel class prototype matching to distill disease-related information from the fundus model to the OCT model and a novel class similarity alignment to enforce consistency between disease distribution of both modalities. Experimental results show that our proposed approach outperforms single-modal, multi-modal, and state-of-the-art distillation methods for retinal disease classification. Code is available at https://github.com/xmed-lab/FDDM.