37 citations · 49 across the 5 of their papers we have counts for
5 papers
Improved statistical benchmarking of digital pathology models using pairwise frames evaluation
Ylaine Gerardin, John Shamshoian, Judy Shen +18
Nested pairwise frames is a method for relative benchmarking of cell or tissue digital pathology models against manual pathologist annotations on a set of sampled patches. At a hig…
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Tan H. Nguyen, Dinkar Juyal, Jin Li +10
Differences in staining and imaging procedures can cause significant color variations in histopathology images, leading to poor generalization when deploying deep-learning models t…
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…
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…