Development of a whole-slide-level segmentation-based dMMR/pMMR deep learning detector for colorectal cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Tong Z
PROVIDER: S-EPMC10709130 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
Publications
Development of a whole-slide-level segmentation-based dMMR/pMMR deep learning detector for colorectal cancer.
Tong Zhou Z Wang Yin Y Bao Xuanwen X Deng Yu Y Lin Bo B Su Ge G Ye Kejun K Dai Xiaomeng X Zhang Hangyu H Liu Lulu L Wang Wenyu W Zheng Yi Y Fang Weijia W Zhao Peng P Ding Peirong P Deng Shuiguang S Xu Xiangming X
iScience 20231115 12
To investigate whole-slide-level prediction in the field of artificial intelligence identification of dMMR/pMMR from hematoxylin and eosin (H&E) in colorectal cancer (CRC), we established a segmentation-based dMMR/pMMR deep learning detector (SPEED). Our model was approximately 1,700 times faster than that of the classification-based model. For the internal validation cohort, our model yielded an overall AUC of 0.989. For the external validation cohort, the model exhibited a high performance, wi ...[more]
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