Nature Communications (Mar 2021)
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
- 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
Affiliations
- James A. Diao
- PathAI, Inc.
- Jason K. Wang
- PathAI, Inc.
- Wan Fung Chui
- PathAI, Inc.
- Victoria Mountain
- PathAI, Inc.
- Sai Chowdary Gullapally
- PathAI, Inc.
- Ramprakash Srinivasan
- PathAI, Inc.
- Richard N. Mitchell
- Program in Health Sciences and Technology, Harvard Medical School
- Benjamin Glass
- PathAI, Inc.
- Sara Hoffman
- PathAI, Inc.
- Sudha K. Rao
- PathAI, Inc.
- Chirag Maheshwari
- PathAI, Inc.
- Abhik Lahiri
- PathAI, Inc.
- Aaditya Prakash
- PathAI, Inc.
- Ryan McLoughlin
- PathAI, Inc.
- Jennifer K. Kerner
- PathAI, Inc.
- Murray B. Resnick
- PathAI, Inc.
- Michael C. Montalto
- PathAI, Inc.
- Aditya Khosla
- PathAI, Inc.
- Ilan N. Wapinski
- PathAI, Inc.
- Andrew H. Beck
- PathAI, Inc.
- Hunter L. Elliott
- PathAI, Inc.
- Amaro Taylor-Weiner
- PathAI, Inc.
- DOI
- https://doi.org/10.1038/s41467-021-21896-9
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 15
Abstract
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 histopathology images using human-interpretable image features.
