Nikhil J. Dhinagar

CALM-VLM improves confidence and reliability in brain MRI classification by calibrating vision-language models and enabling selective prediction.

MAVeRiC-AD improves Alzheimer's disease classification from MRI by combining multiple models with demographic data, achieving higher accuracy across different sites.

This paper introduces a framework using a vision-language model with natural text supervision to perform MRI retrieval, captioning, classification, and visual question answering in Alzheimer's disease research.

This study evaluates parameter-efficient fine-tuning methods for vision transformers in neuroimaging, showing they outperform full fine-tuning and training from scratch with fewer parameters.

The paper uses diffusion models to generate synthetic MRI scans for Alzheimer's disease effect detection, improving classifier performance with limited real data.

This paper harmonizes brain MRI data from multiple sources using 3D CycleGANs and shows improved accuracy in brain age prediction compared to raw data.

This study uses diffusion MRI and BUAN to map microstructural differences in white matter tracts between bipolar disorder patients and controls, finding altered Fractional Anisotropy in specific pathways.

This paper evaluates transfer learning methods for Alzheimer's disease classification using brain MRI, finding that contrastive learning improves performance, especially with limited data.

This paper proposes a 3D CNN for classifying Alzheimer's and Parkinson's disease using T1-weighted MRI, achieving higher ROC-AUC than random forest on multiple datasets.