AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network

Meng Tang^1,2,6,† , Yan Su^2,† , Wei Zhao^3,† , Zubiao Niu^2,† , Banzhan Ruan² , Qinqin Li¹ , You Zheng² , Chenxi Wang² , Bo Zhang^1,2 , Fuxiang Zhou⁴ , Xiaoning Wang⁵ , Hongyan Huang^1,* , Hanping Shi^1,* , Qiang Sun^2,*

¹Beijing Shijitan Hospital of Capital Medical University, Beijing 100038, China
²Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China
³School of Mathematical Sciences, Peking University, Beijing 100871, China
⁴Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
⁵National Clinic Center of Geriatric & State Key Laboratory of Kidney, Chinese PLA General Hospital, Beijing 100853, China
⁶Comprehensive Oncology Department, National Cancer Center/Cancer Hospital, Beijing 100021, China
^†These authors contributed equally to this work
^*Correspondence to:Hongyan Huang , Email:hhongy1999@126.com Hanping Shi , Email:shihp@ccmu.edu.cn Qiang Sun , Email:sunq@bmi.ac.cn

J Mol Cell Biol, Volume 14, Issue 6, June 2022, mjac044,  https://doi.org/10.1093/jmcb/mjac044

Keyword: cell-in-cell structure, artificial intelligence, AIM-CICs, cell death, entosis, convolutional neural network

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