2 citations · 5 across the 18 of their papers we have counts for
22 papers
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…
Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging
Joshua Strong, Pramit Saha, Emma Sun +2
Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarc…
Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals
Pramit Saha, Mohammad Alsharid, Joshua Strong +1
We propose an experience-guided cascaded multi-agent framework for Breast Ultrasound Screening and Diagnosis, called BUSD-Agent, that aims to reduce diagnostic escalation and unnec…
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…
Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew
Can Peng, Yuyuan Liu, Yingyu Yang +3
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but remains challenging when client data are highly heterogen…
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Divyanshu Mishra, Mohammadreza Salehi, Pramit Saha +4
Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due…
DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging
Felix Wagner, Pramit Saha, Harry Anthony +2
Safe deployment of machine learning (ML) models in safety-critical domains such as medical imaging requires detecting inputs with characteristics not seen during training, known as…
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
Divyanshu Mishra, Pramit Saha, He Zhao +4
Accurate standard plane acquisition in fetal ultrasound (US) videos is crucial for fetal growth assessment, anomaly detection, and adherence to clinical guidelines. However, manual…
IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR
Ziyun Liang, Xiaoqing Guo, Wentian Xu +5
Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as `normal'. In the testing phase, they identify patterns that deviate from…
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Pramit Saha, Divyanshu Mishra, Netzahualcoyotl Hernandez-Cruz +4
Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US…
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Can Peng, Qianhui Men, Pramit Saha +5
Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional federated learning methods typically assume…
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
Pramit Saha, Divyanshu Mishra, Felix Wagner +2
Large Vision-Language Models typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is ch…
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation
Jiayuan Zhu, Junde Wu, Cheng Ouyang +2
Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which d…
FOCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics
Pramit Saha, Felix Wagner, Divyanshu Mishra +5
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (P…
MMSummary: Multimodal Summary Generation for Fetal Ultrasound Video
Xiaoqing Guo, Qianhui Men, J. Alison Noble
We present the first automated multimodal summary generation system, MMSummary, for medical imaging video, particularly with a focus on fetal ultrasound analysis. Imitating the exa…
IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI
Ziyun Liang, Xiaoqing Guo, J. Alison Noble +1
Unsupervised anomaly segmentation approaches to pathology segmentation train a model on images of healthy subjects, that they define as the 'normal' data distribution. At inference…
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Felix Wagner, Wentian Xu, Pramit Saha +7
Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment…
Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models
Joshua Strong, Qianhui Men, Alison Noble
Large language models (LLMs) offer a valuable technology for various applications in healthcare. However, their tendency to hallucinate and the existing reliance on proprietary sys…
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…
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
Divyanshu Mishra, He Zhao, Pramit Saha +2
Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecti…
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…