Single-Cell Heterogeneity-Aware Transformer-Guided Multiple Instance Learning for Cancer Aneuploidy Prediction From Whole Slide Histopathology Images
Author:
Affiliation:
1. Department of Computer Science, Stanford University, Stanford, CA, USA
2. Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
Funder
National Cancer Institute
National Institute of Dental and Craniofacial Research
NIH
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10380963/10083175.pdf?arnumber=10083175
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1. Tumor copy number alteration burden is a pan-cancer prognostic factor associated with recurrence and death
2. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy
3. Whole-genome doubling confers unique genetic vulnerabilities on tumour cells
4. AI in health and medicine
5. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
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