A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies-Reference-Cited by-同舟云学术

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Published:2020-11-10 Issue:3 Volume:27 Page:719-728
ISSN:1078-0432
Container-title:Clinical Cancer Research
language:en
Short-container-title:Clin Cancer Res

Author:

Park Jeonghyuk,Jang Bo Gun,Kim Yeong Won,Park Hyunho,Kim Baek-hui,Kim Myeung Ju,Ko Hyungsuk,Gwak Jae Moon,Lee Eun Ji,Chung Yul Ri,Kim Kyungdoc,Myung Jae Kyung,Park Jeong Hwan,Choi Dong Youl,Jung Chang Won,Park Bong-Hee,Jung Kyu-Hwan,Kim Dong-Il

Funder

Korea Institute for Advancement of Technology

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review;Neuropathology and Applied Neurobiology;2024-05-13

2. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies;Laboratory Investigation;2024-05

3. Gastric cancer classification in saliva data samples using Levy search updated rainfall hybrid deep dual-stage BILSTM;Journal of Experimental & Theoretical Artificial Intelligence;2024-01-12

4. A Review on Diagnosing Gastric Cancer using a Tri-Algorithm Gastronet;2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA);2023-12-21

5. Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study;Gastric Cancer;2023-12-14