37 citations · 60 across the 9 of their papers we have counts for
4 papers · 1 filter
Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology
Sai Chowdary Gullapally, Yibo Zhang, Nitin Kumar Mittal +20
Machine learning algorithms have the potential to improve patient outcomes in digital pathology. However, generalization of these tools is currently limited by sensitivity to varia…
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed +6
Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning probl…
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Jin Li, Deepta Rajan, Chintan Shah +7
Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathol…
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Syed Ashar Javed, Dinkar Juyal, Harshith Padigela +3
Multiple Instance Learning (MIL) has been widely applied in pathology towards solving critical problems such as automating cancer diagnosis and grading, predicting patient prognosi…