CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

• URL: http://arxiv.org/abs/2608.16268v1
• Date: Mon, 17 Aug 2026 08:42:41 GMT
• Title: CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
• Abstract summary: CoM$3$eT is a medical vision foundation model that unifies pathology and radiology, sparse and dense predictions, and two- and higher-dimensional inputs by modeling multidimensional context with attention.<n>When adapted across diverse clinical applications, training fewer than 2.5% of parameters achieved performance comparable to full fine-tuning.
• Score: 0.9329311823040861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation. Here, we present CoM$^3$eT (Co-representation Multidimensional Multitask Medical Transformer), a medical vision foundation model that unifies pathology and radiology, sparse and dense predictions, and two- and higher-dimensional inputs by modeling multidimensional context with attention. CoM$^3$eT outperformed other medical foundation models in an open competition spanning five tomographic, four whole-specimen, and three two-dimensional datasets, covering sparse and dense prediction tasks as well as report generation. When adapted across diverse clinical applications, training fewer than 2.5% of parameters achieved performance comparable to full fine-tuning, enabling research without access to high-performance GPU clusters. Applied to federated learning across hospitals, this approach achieved performance comparable to pooled-data training over internet connections and with consumer-grade hardware.

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