MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911289269288960 |
|---|---|
| author | Kong, Shufeng Wang, Zijie Cui, Nuan Tang, Hao Meng, Yihan Wei, Yuanyuan Chen, Feifan Wang, Yingheng Cai, Zhuo Wang, Yaonan Zhang, Yulong Li, Yuzheng Zheng, Zibin Liu, Caihua Liang, Hao |
| author_facet | Kong, Shufeng Wang, Zijie Cui, Nuan Tang, Hao Meng, Yihan Wei, Yuanyuan Chen, Feifan Wang, Yingheng Cai, Zhuo Wang, Yaonan Zhang, Yulong Li, Yuzheng Zheng, Zibin Liu, Caihua Liang, Hao |
| contents | Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels--representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging Kong, Shufeng Wang, Zijie Cui, Nuan Tang, Hao Meng, Yihan Wei, Yuanyuan Chen, Feifan Wang, Yingheng Cai, Zhuo Wang, Yaonan Zhang, Yulong Li, Yuzheng Zheng, Zibin Liu, Caihua Liang, Hao Computer Vision and Pattern Recognition Artificial Intelligence 68T07 Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels--representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks. |
| title | MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07 |
| url | https://arxiv.org/abs/2511.10013 |