117 citations · 135 across the 7 of their papers we have counts for
4 papers · 1 filter
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning
Lantian Zhang, Mohamed Amgad, Lee A. D. Cooper
Data labeling is often the most challenging task when developing computational pathology models. Pathologist participation is necessary to generate accurate labels, and the limitat…
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
Sarah N Dudgeon, Si Wen, Matthew G Hanna +19
Purpose: In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images (WSIs). We focus on data…
HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Sanghoon Lee, Mohamed Amgad, Deepak R. Chittajallu +6
Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algori…
Learning Clinical Outcomes from Heterogeneous Genomic Data Sources
Safoora Yousefi, Amirreza Shaban, Mohamed Amgad +2
Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine la…