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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.21637 |
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| _version_ | 1866914401760575488 |
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| author | Zhang, Di Gong, Zhangpeng Pang, Xiaobo Liu, Jiashuai Lu, Junbo Cui, Hao Ge, Jiusong Zeng, Zhi Yi, Kai Li, Yinghua Liu, Si Yu, Tingsong Wang, Haoran Crispin-Ortuzar, Mireia Yu, Weimiao Li, Chen Gao, Zeyu |
| author_facet | Zhang, Di Gong, Zhangpeng Pang, Xiaobo Liu, Jiashuai Lu, Junbo Cui, Hao Ge, Jiusong Zeng, Zhi Yi, Kai Li, Yinghua Liu, Si Yu, Tingsong Wang, Haoran Crispin-Ortuzar, Mireia Yu, Weimiao Li, Chen Gao, Zeyu |
| contents | Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing models overlook the heterogeneous and non-uniform organization of pathological regions of interest (ROIs) because they rely on natural image backbones not tailored for tissue morphology. Consequently, they often fail to capture the coherent tissue architecture beyond isolated patches, limiting interpretability and clinical relevance. To address these challenges, we present Cross-modal Adaptive Region Encoder (CARE), a foundation model for pathology that automatically partitions WSIs into several morphologically relevant regions. Specifically, CARE employs a two-stage pretraining strategy: (1) a self-supervised unimodal pretraining stage that learns morphological representations from 34,277 whole-slide images (WSIs) without segmentation annotations, and (2) a cross-modal alignment stage that leverages RNA and protein profiles to refine the construction and representation of adaptive regions. This molecular guidance enables CARE to identify biologically relevant patterns and generate irregular yet coherent tissue regions, selecting the most representative area as ROI. CARE supports a broad range of pathology-related tasks, using either the ROI feature or the slide-level feature obtained by aggregating adaptive regions. Based on only one-tenth of the pretraining data typically used by mainstream foundation models, CARE achieves superior average performance across 33 downstream benchmarks, including morphological classification, molecular prediction, and survival analysis, and outperforms other foundation model baselines overall. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21637 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis Zhang, Di Gong, Zhangpeng Pang, Xiaobo Liu, Jiashuai Lu, Junbo Cui, Hao Ge, Jiusong Zeng, Zhi Yi, Kai Li, Yinghua Liu, Si Yu, Tingsong Wang, Haoran Crispin-Ortuzar, Mireia Yu, Weimiao Li, Chen Gao, Zeyu Computer Vision and Pattern Recognition Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing models overlook the heterogeneous and non-uniform organization of pathological regions of interest (ROIs) because they rely on natural image backbones not tailored for tissue morphology. Consequently, they often fail to capture the coherent tissue architecture beyond isolated patches, limiting interpretability and clinical relevance. To address these challenges, we present Cross-modal Adaptive Region Encoder (CARE), a foundation model for pathology that automatically partitions WSIs into several morphologically relevant regions. Specifically, CARE employs a two-stage pretraining strategy: (1) a self-supervised unimodal pretraining stage that learns morphological representations from 34,277 whole-slide images (WSIs) without segmentation annotations, and (2) a cross-modal alignment stage that leverages RNA and protein profiles to refine the construction and representation of adaptive regions. This molecular guidance enables CARE to identify biologically relevant patterns and generate irregular yet coherent tissue regions, selecting the most representative area as ROI. CARE supports a broad range of pathology-related tasks, using either the ROI feature or the slide-level feature obtained by aggregating adaptive regions. Based on only one-tenth of the pretraining data typically used by mainstream foundation models, CARE achieves superior average performance across 33 downstream benchmarks, including morphological classification, molecular prediction, and survival analysis, and outperforms other foundation model baselines overall. |
| title | CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.21637 |