33 citations · 43 across the 4 of their papers we have counts for
5 papers
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
Ming Y. Lu, Dehan Kong, Jana Lipkova +5
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morpholog…
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen +3
The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological featu…
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Richard J. Chen, Ming Y. Lu, Jingwen Wang +4
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, mo…
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
Jingwen Wang, Richard J. Chen, Ming Y. Lu +2
Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a gra…
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Ming Y. Lu, Richard J. Chen, Jingwen Wang +2
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of l…