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| Autori principali: | , , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2509.21358 |
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| _version_ | 1866909806540881920 |
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| author | Jordan, Jason Lor, Mohammadreza Akbari Koulen, Peter Shyu, Mei-Ling Chen, Shu-Ching |
| author_facet | Jordan, Jason Lor, Mohammadreza Akbari Koulen, Peter Shyu, Mei-Ling Chen, Shu-Ching |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification Jordan, Jason Lor, Mohammadreza Akbari Koulen, Peter Shyu, Mei-Ling Chen, Shu-Ching Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21358 |