output
20222025
most citedUncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

138 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing 2022 · q-bio.QMShow all

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q-bio.QM2022★ 2 cited

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Alexander Partin, Thomas Brettin, Yitan Zhu +7

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually bett…

q-bio.QM2022★ 138 cited

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev +13

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-wo…