2 citations · 4 across the 15 of their papers we have counts for
5 papers · 1 filter
RACER: Role-Aligned Competence Estimation for Human-AI Routing
Joshua Strong, Emma Sun, Alexander Capstick +3
Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this problem to unseen experts using a…
Picking the Right Specialist: Attentive Neural Process-based Selection of Task-Specialized Models as Tools for Agentic Healthcare Systems
Pramit Saha, Joshua Strong, Mohammad Alsharid +2
Task-specialized models form the backbone of agentic healthcare systems, enabling the agents to answer clinical queries across tasks such as disease diagnosis, localization, and re…
FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents
Pramit Saha, Joshua Strong, Divyanshu Mishra +2
Federated learning (FL) allows collaborative model training across healthcare sites without sharing sensitive patient data. However, real-world FL deployment is often hindered by c…
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection
Pramit Saha, Divyanshu Mishra, Felix Wagner +2
Multimodal Federated Learning (MMFL) utilizes multiple modalities in each client to build a more powerful Federated Learning (FL) model than its unimodal counterpart. However, the…
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data
Pramit Saha, Divyanshu Mishra, J. Alison Noble
The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabe…