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Development and Validation of a Few-Shot Rapid Screening Model for Gastrointestinal Cancers Using AGI Large Vision Models.

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Title: Development and Validation of a Few-Shot Rapid Screening Model for Gastrointestinal Cancers Using AGI Large Vision Models.
Authors: Lijue, Liu1 ljliu@csu.edu.cn, Fangjie, Yin1 yinfangjie2023@126.com, Genjian, Yang2 457706420@qq.com, Qi, Li3 15201232918@163.com, Siya, Li4 1004297233@qq.com, Teng, Pan5 2570758402@qq.com, Ting, Liu6 liuting1981_2005@126.com, Jin, Tang1,7 tjin@csu.edu.cn, Ruijie, Ming8 ming_ruijie@cqu.edu.cn, Yu, Song9 syandf@163.com, Xue, Feng10 fengxuenku@163.com, Dan, Wang11 dan.7.wang@kcl.ac.uk, Xingang, Zhou6 zhouxg1980@126.com, Wenbai, Chen2 chenwb@bistu.edu.cn, Jinhai, Deng11,12,13 jinhaideng_kcl@163.com
Source: Computer Science & Information Systems. Apr2026, Vol. 23 Issue 2, p801-825. 25p.
Subjects: Gastrointestinal cancer, Tissue analysis, Machine learning, Computer-assisted image analysis (Medicine), Medical screening
Abstract: Existing deep learning models in digital pathology typically require extensive labeled data and show limited generalization across organs. In contrast, large vision models exhibit effective feature extraction capabilities, enabling pathological image analysis for gastrointestinal cancer with relatively small sample sizes. In this study, we developed a screening framework leveraging a large vision model for coarse-grained classification of gastric and colorectal tissues. The model was evaluated on multicenter cohorts and under limited-data conditions. Using labeled tiles from only 76 whole-slide images, the model achieved class-averaged sensitivity and precision of 0.9816 and 0.9808 on the internal test set, and 0.9161 and 0.9179 on the external test set. When trained with only 200 tiles per class from 20 wholeslide images, the model maintained comparable performance, achieving sensitivity and precision of 0.9548 and 0.9518. These findings suggest that the model has reliable performance across multicenter cohorts and potential applicability in clinical pathology workflows. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Existing deep learning models in digital pathology typically require extensive labeled data and show limited generalization across organs. In contrast, large vision models exhibit effective feature extraction capabilities, enabling pathological image analysis for gastrointestinal cancer with relatively small sample sizes. In this study, we developed a screening framework leveraging a large vision model for coarse-grained classification of gastric and colorectal tissues. The model was evaluated on multicenter cohorts and under limited-data conditions. Using labeled tiles from only 76 whole-slide images, the model achieved class-averaged sensitivity and precision of 0.9816 and 0.9808 on the internal test set, and 0.9161 and 0.9179 on the external test set. When trained with only 200 tiles per class from 20 wholeslide images, the model maintained comparable performance, achieving sensitivity and precision of 0.9548 and 0.9518. These findings suggest that the model has reliable performance across multicenter cohorts and potential applicability in clinical pathology workflows. [ABSTRACT FROM AUTHOR]
ISSN:18200214
DOI:10.2298/CSIS251130024L