Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology

no code implementations • 3 May 2023 • Sai Chowdary Gullapally, Yibo Zhang, Nitin Kumar Mittal, Deeksha Kartik, Sandhya Srinivasan, Kevin Rose, Daniel Shenker, Dinkar Juyal, Harshith Padigela, Raymond Biju, Victor Minden, Chirag Maheshwari, Marc Thibault, Zvi Goldstein, Luke Novak, Nidhi Chandra, Justin Lee, Aaditya Prakash, Chintan Shah, John Abel, Darren Fahy, Amaro Taylor-Weiner, Anand Sampat

Machine learning algorithms have the potential to improve patient outcomes in digital pathology.

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

no code implementations • 23 Mar 2023 • Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed, Harshith Padigela, Chintan Shah, Anand Sampat, Archit Khosla, John Abel, Amaro Taylor-Weiner

We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets.

Contrastive Learning imbalanced classification +1

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology

no code implementations • 3 Jun 2022 • Syed Ashar Javed, Dinkar Juyal, Harshith Padigela, Amaro Taylor-Weiner, Limin Yu, Aaditya Prakash

Our Additive MIL models enable spatial credit assignment such that the contribution of each region in the image can be exactly computed and visualized.

Decision Making Multiple Instance Learning

Investigating the Successes and Failures of BERT for Passage Re-Ranking

no code implementations • 5 May 2019 • 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.

Passage Re-Ranking Re-Ranking +1