138 citations · 144 across the 3 of their papers we have counts for
3 papers
Deep Learning Generates Synthetic Cancer Histology for Explainability and Education
James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon +20
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or excee…
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…
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…