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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/2605.19491 |
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| _version_ | 1866917527672586240 |
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| author | Ge, Jiusong Zhan, Yingkang Zhao, Wenjie Zhang, Di Wang, Ke Liu, Jiashuai Yang, Chunze Li, Chengzu Zhang, Jian Dong, Yuxin Zhang, Ni Liu, Qidong Crispin-Ortuzar, Mireia Fu, Huazhu Li, Chen Gao, Zeyu |
| author_facet | Ge, Jiusong Zhan, Yingkang Zhao, Wenjie Zhang, Di Wang, Ke Liu, Jiashuai Yang, Chunze Li, Chengzu Zhang, Jian Dong, Yuxin Zhang, Ni Liu, Qidong Crispin-Ortuzar, Mireia Fu, Huazhu Li, Chen Gao, Zeyu |
| contents | Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and aggregates them for slide-level prediction. However, such exhaustive patch-level processing is computationally expensive, severely limiting the efficiency and scalability of WSI analysis. To address this challenge, we propose PathCTM (a Pathology-oriented Continuous Thought Model) that enables token-efficient scale-space continuous reasoning for gigapixel WSIs. PathCTM formulates diagnostic inference as a dynamic sequential information pursuit. It progressively transitions from low-magnification global to high-magnification local inspection, and adaptively terminates inference when sufficient evidence is gathered to effectively bound decision uncertainty. Specifically, it uses conditional computation for dynamic scale switching with attention-guided region pruning, coupled with confidence-aware early stopping. Extensive experiments demonstrate that, compared with standard MIL-based methods, PathCTM reduces the number of required image patches by 95.95% and shortens inference time by approximately 95.62%, while maintaining AUC without degradation. Code is available at https://github.com/JSGe-AI/PathCTM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19491 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning Ge, Jiusong Zhan, Yingkang Zhao, Wenjie Zhang, Di Wang, Ke Liu, Jiashuai Yang, Chunze Li, Chengzu Zhang, Jian Dong, Yuxin Zhang, Ni Liu, Qidong Crispin-Ortuzar, Mireia Fu, Huazhu Li, Chen Gao, Zeyu Computer Vision and Pattern Recognition Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and aggregates them for slide-level prediction. However, such exhaustive patch-level processing is computationally expensive, severely limiting the efficiency and scalability of WSI analysis. To address this challenge, we propose PathCTM (a Pathology-oriented Continuous Thought Model) that enables token-efficient scale-space continuous reasoning for gigapixel WSIs. PathCTM formulates diagnostic inference as a dynamic sequential information pursuit. It progressively transitions from low-magnification global to high-magnification local inspection, and adaptively terminates inference when sufficient evidence is gathered to effectively bound decision uncertainty. Specifically, it uses conditional computation for dynamic scale switching with attention-guided region pruning, coupled with confidence-aware early stopping. Extensive experiments demonstrate that, compared with standard MIL-based methods, PathCTM reduces the number of required image patches by 95.95% and shortens inference time by approximately 95.62%, while maintaining AUC without degradation. Code is available at https://github.com/JSGe-AI/PathCTM. |
| title | Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.19491 |