Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images-Reference-Cited by-同舟云学术

Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images

Published:2024-06 Issue:6 Volume:43 Page:2266-2278
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Shao Wei¹^ORCID,Shi Hang¹,Liu Jianxin¹,Zuo Yingli¹,Sun Liang¹^ORCID,Xia Tiansong²,Chen Wanyuan³,Wan Peng¹^ORCID,Sheng Jianpeng⁴,Zhu Qi¹^ORCID,Zhang Daoqiang¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, and the MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. First Affiliated Hospital of Nanjing Medical University, Nanjing, China

3. Department of Pathology, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China

4. First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Key Research and Development Plan of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10547147/10423049.pdf?arnumber=10423049

Reference50 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

2. Predicting tumour mutational burden from histopathological images using multiscale deep learning

3. Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

4. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

5. High-speed light-sheet microscopy for the in-situ acquisition of volumetric histological images of living tissue

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