Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

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Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this work, we develop and evaluate a joint outcome and biomarker supervised (estrogen receptor expression and ERBB...

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Bibliographic Details
Main Authors: Dmitrii Bychkov, Heikki Joensuu, Stig Nordling, Aleksei Tiulpin, Hakan Kücükel, Mikael Lundin, Harri Sihto, Jorma Isola, Tiina Lehtimäki, Pirkko-Liisa Kellokumpu-Lehtinen, Karl von Smitten, Johan Lundin, Nina Linder
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Journal of Pathology Informatics
Subjects:
Breast cancer
Convolutional neural networks
Digital pathology
ERBB2 gene
Estrogen receptor
Multitask deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353922007659

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http://www.sciencedirect.com/science/article/pii/S2153353922007659

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