MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913134353055744 |
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| author | Cho, Seungik Li, Anqi Qiu, Wei |
| author_facet | Cho, Seungik Li, Anqi Qiu, Wei |
| contents | Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing modalities at training and test time, and sample-specific variation in modality contributions. We introduce MedMIX, a multimodal framework that combines intra-modality expert fusion, learned inter-modality fusion, and training-only large--small model collaboration for robust medical prediction under incomplete modalities. Within each modality, MedMIX aggregates complementary embeddings from multiple small expert models; across modalities, it performs learned fusion over available modalities; and during training, it leverages large teacher models to improve deployed representations without additional inference cost. Across three heterogeneous benchmarks (OpenI, MIMIC-IV-MM, and MMIST-ccRCC), MedMIX achieves consistently strong performance while remaining robust under controlled missing-modality perturbations, and further demonstrates sustained robustness under cross-cohort shift on MIMIC-III. These results highlight MedMIX as a practical framework that unifies within-modality expert collaboration, sample-specific cross-modality fusion, and efficient large--small model collaboration while remaining robust to incomplete modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16639 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis Cho, Seungik Li, Anqi Qiu, Wei Machine Learning Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing modalities at training and test time, and sample-specific variation in modality contributions. We introduce MedMIX, a multimodal framework that combines intra-modality expert fusion, learned inter-modality fusion, and training-only large--small model collaboration for robust medical prediction under incomplete modalities. Within each modality, MedMIX aggregates complementary embeddings from multiple small expert models; across modalities, it performs learned fusion over available modalities; and during training, it leverages large teacher models to improve deployed representations without additional inference cost. Across three heterogeneous benchmarks (OpenI, MIMIC-IV-MM, and MMIST-ccRCC), MedMIX achieves consistently strong performance while remaining robust under controlled missing-modality perturbations, and further demonstrates sustained robustness under cross-cohort shift on MIMIC-III. These results highlight MedMIX as a practical framework that unifies within-modality expert collaboration, sample-specific cross-modality fusion, and efficient large--small model collaboration while remaining robust to incomplete modalities. |
| title | MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.16639 |