An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer

Summary: Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification,...

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Bibliographic Details
Main Authors: Xueyi Zheng, Bingzhong Jing, Zihan Zhao, Ruixuan Wang, Xinke Zhang, Haohua Chen, Shuyang Wu, Yan Sun, Jiangyu Zhang, Hongmei Wu, Dan Huang, Wenbiao Zhu, Jianning Chen, Qinghua Cao, Hong Zeng, Jinling Duan, Yuanliang Luo, Zhicheng Li, Wuhao Lin, Runcong Nie, Yishu Deng, Jingping Yun, Chaofeng Li, Dan Xie, Muyan Cai
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:iScience
Subjects:
Pathology
Diagnostics
Cancer
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004224004644

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http://www.sciencedirect.com/science/article/pii/S2589004224004644

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