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AI and Pathology: Steering Treatment and Predicting Outcomes

2022-06-15 14:55:30
Rajarsi Gupta, Jakub Kaczmarzyk, Soma Kobayashi, Tahsin Kurc, Joel Saltz
arXiv_AI
       
Abstract

The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses. We describe the rich set of application challenges related to tissue interpretation and survey AI methods currently used to address these challenges. We focus on a particular class of targeted human tissue analysis - histopathology - aimed at quantitative characterization of disease state, patient outcome prediction and treatment steering.

Abstract (translated)

URL

https://arxiv.org/abs/2206.07573

PDF

https://arxiv.org/pdf/2206.07573.pdf

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