{"ID":2868063,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21358","arxiv_id":"2509.21358","title":"MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification","abstract":"This study aimed to enhance disease classification accuracy from retinal fundus images by integrating fine-grained image features and global textual context using a novel multimodal deep learning architecture. Existing multimodal large language models (MLLMs) often struggle to capture low-level spatial details critical for diagnosing retinal diseases such as glaucoma, diabetic retinopathy, and retinitis pigmentosa. This model development and validation study was conducted on 1,305 fundus image-text pairs compiled from three public datasets (FIVES, HRF, and StoneRounds), covering acquired and inherited retinal diseases, and evaluated using classification accuracy and F1-score. The MDF-MLLM integrates skip features from four U-Net encoder layers into cross-attention blocks within a LLaMA 3.2 11B MLLM. Vision features are patch-wise projected and fused using scaled cross-attention and FiLM-based U-Net modulation. Baseline MLLM achieved 60% accuracy on the dual-type disease classification task. MDF-MLLM, with both U-Net and MLLM components fully fine-tuned during training, achieved a significantly higher accuracy of 94%, representing a 56% improvement. Recall and F1-scores improved by as much as 67% and 35% over baseline, respectively. Ablation studies confirmed that the multi-depth fusion approach contributed to substantial gains in spatial reasoning and classification, particularly for inherited diseases with rich clinical text. MDF-MLLM presents a generalizable, interpretable, and modular framework for fundus image classification, outperforming traditional MLLM baselines through multi-scale feature fusion. The architecture holds promise for real-world deployment in clinical decision support systems. Future work will explore synchronized training techniques, a larger pool of diseases for more generalizability, and extending the model for segmentation tasks.","short_abstract":"This study aimed to enhance disease classification accuracy from retinal fundus images by integrating fine-grained image features and global textual context using a novel multimodal deep learning architecture. Existing multimodal large language models (MLLMs) often struggle to capture low-level spatial details critical...","url_abs":"https://arxiv.org/abs/2509.21358","url_pdf":"https://arxiv.org/pdf/2509.21358v1","authors":"[\"Jason Jordan\",\"Mohammadreza Akbari Lor\",\"Peter Koulen\",\"Mei-Ling Shyu\",\"Shu-Ching Chen\"]","published":"2025-09-21T05:46:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}