2 citations · 5 across the 18 of their papers we have counts for
8 papers · 1 filter
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…