activity
20222024
most citedPLUTO: Pathology-Universal Transformer

8 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2024

Learning biologically relevant features in a pathology foundation model using sparse autoencoders

Nhat Minh Le, Ciyue Shen, Neel Patel +13

Pathology plays an important role in disease diagnosis, treatment decision-making and drug development. Previous works on interpretability for machine learning models on pathology…

eess.IV2024★ 8 cited

PLUTO: Pathology-Universal Transformer

Dinkar Juyal, Harshith Padigela, Chintan Shah +30

Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique chal…

cs.CV2023★ 6 cited

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…

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…

eess.IV2022

Multi stain graph fusion for multimodal integration in pathology

Chaitanya Dwivedi, Shima Nofallah, Maryam Pouryahya +8

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN bas…