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Artificial Intelligence in Lung Cancer Pathology Image Analysis.

Authors :
Wang, Shidan
Yang, Donghan M.
Rong, Ruichen
Zhan, Xiaowei
Fujimoto, Junya
Liu, Hongyu
Minna, John
Wistuba, Ignacio Ivan
Xie, Yang
Xiao, Guanghua
Source :
Cancers. Nov2019, Vol. 11 Issue 11, p1673-1673. 1p.
Publication Year :
2019

Abstract

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20726694
Volume :
11
Issue :
11
Database :
Academic Search Index
Journal :
Cancers
Publication Type :
Academic Journal
Accession number :
139863476
Full Text :
https://doi.org/10.3390/cancers11111673