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Transcriptomic learning for digital pathology
[article]
2019
bioRxiv
pre-print
Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and the prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides, has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that
doi:10.1101/760173
fatcat:mb55w3o4djgvhdbaj2r7mltgey