10 citations · 14 across the 18 of their papers we have counts for
7 papers · 1 filter
Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
Brad Ye, Bulent Soykan, Gulsah Hancerliogullari Koksalmis +2
Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, developing reliable conversion pred…
FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning
Mahad Ali, Laura J. Brattain
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates under statistical heterogeneity.…
Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework
Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1
Cognitive decline is highly heterogeneous across individuals, which complicates prognosis, trial design, and treatment planning. We present the Personalized Cognitive Decline Asses…
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1
Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accu…
GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses
Parham Kebria, Soheil Sabri, Laura J Brattain
Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabil…
Hybrid Diffusion Model for Breast Ultrasound Image Augmentation
Farhan Fuad Abir, Sanjeda Sara Jennifer, Niloofar Yousefi +1
We propose a hybrid diffusion-based augmentation framework to overcome the critical challenge of ultrasound data augmentation in breast ultrasound (BUS) datasets. Unlike convention…
BiSe-Unet: A Lightweight Dual-path U-Net with Attention-refined Context for Real-time Medical Image Segmentation
M Iffat Hossain, Laura Brattain
During image-guided procedures, real-time image segmentation is often required. This demands lightweight AI models that can operate on resource-constrained devices. One important u…