activity
20222024
most citedSynthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Composition of Experts: A Modular Compound AI System Leveraging Large Language Models

Swayambhoo Jain, Ravi Raju, Bo Li +8

Large Language Models (LLMs) have achieved remarkable advancements, but their monolithic nature presents challenges in terms of scalability, cost, and customization. This paper int…

eess.IV2023★ 1 cited

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…

cs.CV2023★ 5 cited

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…

cs.CV2023

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…

cs.CV2022★ 1 cited

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…