E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image-Reference-Cited by-同舟云学术

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image

Published:2023-08 Issue: Volume:88 Page:102837
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Cao Lei^ORCID,Wang Jie,Zhang Yuanyuan,Rong Zhiwei^ORCID,Wang Meng,Wang Liuying^ORCID,Ji Jianxin,Qian Youhui,Zhang Liuchao,Wu Hao,Song Jiali,Liu Zheng^ORCID,Wang Wenjie,Li Shuang,Wang Peiyu,Xu Zhenyi,Zhang Jingyuan,Zhao Liang^ORCID,Wang Hang,Sun Mengting,Huang Xing,Yin Rong,Lu Yuhong,Liu Ziqian,Deng Kui^ORCID,Wang Gongwei,Qiu Mantang^ORCID,Li Kang^ORCID,Wang Jun,Hou Yan

Funder

National Natural Science Foundation of China

Harbin Medical University

Harbin Engineering University

Vanderbilt University Medical Center

Peking University

Fundamental Research Funds for the Central Universities

Johnson and Johnson Vision Care

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

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