An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in...
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer
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Author / Creator
Gao, Yuan , Ventura-Diaz, Sofia , Wang, Xin , He, Muzhen , Xu, Zeyan , Weir, Arlene , Zhou, Hong-Yu , Zhang, Tianyu , van Duijnhoven, Frederieke H. , Han, Luyi , Li, Xiaomei , D’Angelo, Anna , Longo, Valentina , Liu, Zaiyi , Teuwen, Jonas , Kok, Marleen , Beets-Tan, Regina , Horlings, Hugo M. , Tan, Tao and Mann, Ritse
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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More information
Scope and Contents
Contents
Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-mo...
Alternative Titles
Full title
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c0d62ff4a3f64984a6d212ffdc266b26
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c0d62ff4a3f64984a6d212ffdc266b26
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-53450-8
