37 citations · 78 across the 8 of their papers we have counts for
8 papers
PLUTO-4: Frontier Pathology Foundation Models
Harshith Padigela, Shima Nofallah, Atchuth Naveen Chilaparasetti +11
Foundation models trained on large-scale pathology image corpora have demonstrated strong transfer capabilities across diverse histopathology tasks. Building on this progress, we i…
ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows
Harshith Padigela, Chintan Shah, Dinkar Juyal
In this report, we present ML-Dev-Bench, a benchmark aimed at testing agentic capabilities on applied Machine Learning development tasks. While existing benchmarks focus on isolate…
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…
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…
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…
Investigating the Successes and Failures of BERT for Passage Re-Ranking
Harshith Padigela, Hamed Zamani, W. Bruce Croft
The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results pro…