A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Cai, Yu Jin, Cheng Ma, Jiabo Zhou, Fengtao Xu, Yingxue Guo, Zhengrui Wang, Yihui Zhang, Zhengyu Liang, Ling Tan, Yonghao Dong, Pingcheng Cai, Du Tang, On Ki Zhao, Chenglong Wang, Xi Yang, Can Xu, Yali Cui, Jing Li, Zhenhui Chan, Ronald Cheong Kin Liu, Yueping Gao, Feng Zhang, Xiuming Liang, Li Chen, Hao Cheng, Kwang-Ting |
| author_facet | Cai, Yu Jin, Cheng Ma, Jiabo Zhou, Fengtao Xu, Yingxue Guo, Zhengrui Wang, Yihui Zhang, Zhengyu Liang, Ling Tan, Yonghao Dong, Pingcheng Cai, Du Tang, On Ki Zhao, Chenglong Wang, Xi Yang, Can Xu, Yali Cui, Jing Li, Zhenhui Chan, Ronald Cheong Kin Liu, Yueping Gao, Feng Zhang, Xiuming Liang, Li Chen, Hao Cheng, Kwang-Ting |
| contents | Pathology foundation models (PFMs) have enabled robust generalization in computational pathology through large-scale datasets and expansive architectures, but their substantial computational cost, particularly for gigapixel whole slide images, limits clinical accessibility and scalability. Here, we present LitePath, a deployment-friendly foundational framework designed to mitigate model over-parameterization and patch level redundancy. LitePath integrates LiteFM, a compact model distilled from three large PFMs (Virchow2, H-Optimus-1 and UNI2) using 190 million patches, and the Adaptive Patch Selector (APS), a lightweight component for task-specific patch selection. The framework reduces model parameters by 28x and lowers FLOPs by 403.5x relative to Virchow2, enabling deployment on low-power edge hardware such as the NVIDIA Jetson Orin Nano Super. On this device, LitePath processes 208 slides per hour, 104.5x faster than Virchow2, and consumes 0.36 kWh per 3,000 slides, 171x lower than Virchow2 on an RTX3090 GPU. We validated accuracy using 37 cohorts across four organs and 26 tasks (26 internal, 9 external, and 2 prospective), comprising 15,672 slides from 9,808 patients disjoint from the pretraining data. LitePath ranks second among 19 evaluated models and outperforms larger models including H-Optimus-1, mSTAR, UNI2 and GPFM, while retaining 99.71% of the AUC of Virchow2 on average. To quantify the balance between accuracy and efficiency, we propose the Deployability Score (D-Score), defined as the weighted geometric mean of normalized AUC and normalized FLOP, where LitePath achieves the highest value, surpassing Virchow2 by 10.64%. These results demonstrate that LitePath enables rapid, cost-effective and energy-efficient pathology image analysis on accessible hardware while maintaining accuracy comparable to state-of-the-art PFMs and reducing the carbon footprint of AI deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14010 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Deployment-Friendly Foundational Framework for Efficient Computational Pathology Cai, Yu Jin, Cheng Ma, Jiabo Zhou, Fengtao Xu, Yingxue Guo, Zhengrui Wang, Yihui Zhang, Zhengyu Liang, Ling Tan, Yonghao Dong, Pingcheng Cai, Du Tang, On Ki Zhao, Chenglong Wang, Xi Yang, Can Xu, Yali Cui, Jing Li, Zhenhui Chan, Ronald Cheong Kin Liu, Yueping Gao, Feng Zhang, Xiuming Liang, Li Chen, Hao Cheng, Kwang-Ting Computer Vision and Pattern Recognition Artificial Intelligence Pathology foundation models (PFMs) have enabled robust generalization in computational pathology through large-scale datasets and expansive architectures, but their substantial computational cost, particularly for gigapixel whole slide images, limits clinical accessibility and scalability. Here, we present LitePath, a deployment-friendly foundational framework designed to mitigate model over-parameterization and patch level redundancy. LitePath integrates LiteFM, a compact model distilled from three large PFMs (Virchow2, H-Optimus-1 and UNI2) using 190 million patches, and the Adaptive Patch Selector (APS), a lightweight component for task-specific patch selection. The framework reduces model parameters by 28x and lowers FLOPs by 403.5x relative to Virchow2, enabling deployment on low-power edge hardware such as the NVIDIA Jetson Orin Nano Super. On this device, LitePath processes 208 slides per hour, 104.5x faster than Virchow2, and consumes 0.36 kWh per 3,000 slides, 171x lower than Virchow2 on an RTX3090 GPU. We validated accuracy using 37 cohorts across four organs and 26 tasks (26 internal, 9 external, and 2 prospective), comprising 15,672 slides from 9,808 patients disjoint from the pretraining data. LitePath ranks second among 19 evaluated models and outperforms larger models including H-Optimus-1, mSTAR, UNI2 and GPFM, while retaining 99.71% of the AUC of Virchow2 on average. To quantify the balance between accuracy and efficiency, we propose the Deployability Score (D-Score), defined as the weighted geometric mean of normalized AUC and normalized FLOP, where LitePath achieves the highest value, surpassing Virchow2 by 10.64%. These results demonstrate that LitePath enables rapid, cost-effective and energy-efficient pathology image analysis on accessible hardware while maintaining accuracy comparable to state-of-the-art PFMs and reducing the carbon footprint of AI deployment. |
| title | A Deployment-Friendly Foundational Framework for Efficient Computational Pathology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.14010 |