Computer-Aided-Detection

Varian will support my research project entitled “Longitudinal CBCT radiomics analysis for lung cancer radiotherapy response and prognosis prediction” with $230,000 over 2 years. This is the first research grant from Varian to the Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University. This project can potentially impact the clinical practice of lung cancer patients by using standard imaging modalities (CBCT and 4D-CBCT) to provide early prediction of prognosis and toxicity. ...

MICCAI'22 Paper | CMPB'21 Paper | CIRDataset This library serves as a one-stop solution for analyzing datasets using clinically-interpretable radiomics (CIR) in cancer imaging (https://github.com/choilab-jefferson/CIR). The primary motivation for this comes from our collaborators in radiology and radiation oncology inquiring about the importance of clinically-reported features in state-of-the-art deep learning malignancy/recurrence/treatment response prediction algorithms. Previous methods have performed such prediction tasks but without robust attribution to any clinically reported/actionable features (see extensive literature on the sensitivity of attribution methods to hyperparameters). This motivated us to curate datasets by annotating clinically-reported features at the voxel/vertex level on public datasets (using our published advanced mathematical algorithms) and relating these to prediction tasks (bypassing the “flaky” attribution schemes). With the release of these comprehensively-annotated datasets, we hope that previous malignancy prediction methods can also validate their explanations and provide clinically-actionable insights. We also provide strong end-to-end baselines for extracting these hard-to-compute clinically-reported features and using these in different prediction tasks. ...

Invited talk in GIST, Nov 2014

2010 IEEE ICIP