GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhang, Hantao Guo, Weidong Liu, Yuhe Yang, Jiancheng Bhagavan, Sathvik Shi, Danli Xu, Mingda Fua, Pascal |
| author_facet | Zhang, Hantao Guo, Weidong Liu, Yuhe Yang, Jiancheng Bhagavan, Sathvik Shi, Danli Xu, Mingda Fua, Pascal |
| contents | Data-driven medical AI is traditionally formulated as a discriminative mapping from input $X$ to output $Y$ via a learned function $f$, which does not generalize well across heterogeneous data and modalities encountered in real-world clinical settings. In this work, we propose a fundamentally different, generative paradigm. We model the joint distribution $P(X,Y)$ using diffusion models and reframe inference as a test-time output optimization problem. By guiding the generative process to match observed inputs, our framework enables flexible, gradient-based conditioning at inference time without architectural changes or retraining, effectively supporting arbitrary and previously unseen combinations of observations. Extensive experiments demonstrate strong performance across standard and cross-modality medical image segmentation, few-shot segmentation with only 2 or 4 training samples, degraded-input segmentation, shape completion from sparse and partial observations, and zero-shot application to demonstrate generality. To support these evaluations, we curated and released a large-scale text-shape dataset derived from MedShapeNet. Our results highlight the versatility of generative joint modeling as a foundation for reusable, task-agnostic medical AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10645 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks Zhang, Hantao Guo, Weidong Liu, Yuhe Yang, Jiancheng Bhagavan, Sathvik Shi, Danli Xu, Mingda Fua, Pascal Computer Vision and Pattern Recognition Data-driven medical AI is traditionally formulated as a discriminative mapping from input $X$ to output $Y$ via a learned function $f$, which does not generalize well across heterogeneous data and modalities encountered in real-world clinical settings. In this work, we propose a fundamentally different, generative paradigm. We model the joint distribution $P(X,Y)$ using diffusion models and reframe inference as a test-time output optimization problem. By guiding the generative process to match observed inputs, our framework enables flexible, gradient-based conditioning at inference time without architectural changes or retraining, effectively supporting arbitrary and previously unseen combinations of observations. Extensive experiments demonstrate strong performance across standard and cross-modality medical image segmentation, few-shot segmentation with only 2 or 4 training samples, degraded-input segmentation, shape completion from sparse and partial observations, and zero-shot application to demonstrate generality. To support these evaluations, we curated and released a large-scale text-shape dataset derived from MedShapeNet. Our results highlight the versatility of generative joint modeling as a foundation for reusable, task-agnostic medical AI systems. |
| title | GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.10645 |