Deep learning models for cancer stem cell detection: a brief review

Frontiers in Immunology

Immunology

Seungman Park¹  Jingchun Chen²  Lingyun Xu³  Xindi Li³ 

[1] Department of Mechanical Engineering, University of Nevada, Las Vegas, Las Vegas, NV, United States;Nevada Institute for Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, NV, United States;School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, China;

关键词: cancer stem cells (CSCs);    artificial intelligence (AI);    deep learning;    convolutional neural network (CNN);    image classification;   

DOI  :  10.3389/fimmu.2023.1214425

 received in 2023-05-02, accepted in 2023-06-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Xu, Li and Park