Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: James A. Diao, Jason K. Wang, Wan Fung Chui, Victoria Mountain, Sai Chowdary Gullapally, Ramprakash Srinivasan, Richard N. Mitchell, Benjamin Glass, Sara Hoffman, Sudha K. Rao, Chirag Maheshwari, Abhik Lahiri, Aaditya Prakash, Ryan McLoughlin, Jennifer K. Kerner, Murray B. Resnick, Michael C. Montalto, Aditya Khosla, Ilan N. Wapinski, Andrew H. Beck, Hunter L. Elliott, Amaro Taylor-Weiner
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/05d507904fe140cd941a3a33245b3716
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Ejemplares similares