What I work on
My work sits at the intersection of medical image processing and machine learning, focused on making perfusion MRI a more objective tool for neuro-oncology.
Quantitative DCE-MRI
Perfusion imaging of brain tumors to derive objective, reproducible biomarkers for grading and treatment assessment.
Vessel Segmentation
Swin UNETR, Attention U-Net and U-Net pipelines for automatic segmentation of large blood vessels in tumor MRI.
Glioma Classification
Differentiating glioblastoma, diffuse midline glioma and grade-3 glioma from quantitative perfusion parameters.
Radiomics & Texture
PyRadiomics workflows — 107 features per sequence, 851 with wavelet decomposition — paired with ML classifiers.
MRI Contrast Agent Optimization
Collaborative work with IIT Mandi — MRI scanning of contrast-agent samples (n = 27) and T1/T2 relaxivity computation for selecting optimal agents for specific MRI applications.
Brain–Computer Interfaces
EEG-based study of advertisement impact on consumer preference and mental stress detection, using SVM classification (M.Tech research).