2 citations · 5 across the 12 of their papers we have counts for
4 papers · 2 filters
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…
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…
Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection
Harry Anthony, Konstantinos Kamnitsas
Reliable use of deep neural networks (DNNs) for medical image analysis requires methods to identify inputs that differ significantly from the training data, called out-of-distribut…
Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
Wentian Xu, Matthew Moffat, Thalia Seale +8
Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determine…