Clinically-Interpretable Radiomics

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. PreviousContinue reading “Clinically-Interpretable Radiomics”

Lung Cancer Screening Radiomics

A comprehensive framework for lung cancer screening radiomics using LIDC-IDRI and LUNGx dataset. Data preprocessing – download data, conversion, etc. Radiomics feature extraction including spiculation features AutoML model building and validation Source code https://github.com/choilab-jefferson/LungCancerScreeningRadiomics Publications Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos,Continue reading “Lung Cancer Screening Radiomics”

Hiring a Postdoctoral Fellow

Postdoctoral Fellow – Developing Clinically Interpretable Medical Imaging AI in Radiation Therapy PI: Wookjin Choi, Ph.D. <Wookjin.Choi@jefferson.edu> Assistant Professor of Radiation Oncology, Thomas Jefferson University 2 Years Responsibilities POST-DOCTORAL POSITION, DEPARTMENT OF RADIATION ONCOLOGY: Thomas Jefferson University is now accepting applications for a post-doctoral fellow in the Department of Radiation Oncology with the Choi lab. Continue reading “Hiring a Postdoctoral Fellow”

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy The authors wish it to be known that, in their opinion, Jung Hun Oh and Wookjin Choi should be regarded as Joint First Authors. https://academic.oup.com/bioinformatics/article/37/Supplement_1/i443/6319702 Abstract Motivation Convolutional neural networks (CNNs) have achieved great success in the areas of image processingContinue reading “PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma”

Fourth place Winner on AI Tracks at Sea Challenge

We won 4th place in the Artificial Intelligence (AI) Tracks at Sea Challenge. https://www.challenge.gov/challenge/AI-tracks-at-sea/This national competition is organized by the U.S. Navy. VSU TrojanOne Team: Jose Diaz, Curtrell Trott, Advisor: Ju Wang, Wookjin Choi The $200,000 prize was distributed among five winning teams, which submitted full working solutions, and three runners-up, which submitted partial workingContinue reading “Fourth place Winner on AI Tracks at Sea Challenge”

Automatic motion tracking system for analysis of insect behavior

Darrin Gladman, Jehu Osegbe, Wookjin Choi*, and Joon Suk Lee “Automatic motion tracking system for analysis of insect behavior”, Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115102W (21 August 2020); https://doi.org/10.1117/12.2568804 *Corresponding author Abstract We present a multi-object tracking system to track small insects such as ants and bees. Motion-based object tracking recognizes the movements of objects in videosContinue reading “Automatic motion tracking system for analysis of insect behavior”

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

Choi, W., Nadeem, S., Alam, S. R., Deasy, J. O., Tannenbaum, A., & Lu, W. (2020). Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening. Computer Methods and Programs in Biomedicine, 105839. https://doi.org/10.1016/j.cmpb.2020.105839 Source codes: https://github.com/choilab-jefferson/LungCancerScreeningRadiomics Highlights A novel interpretable spiculation feature is presented, computed using the area distortion metric from spherical conformal (angle-preserving) parameterization. AContinue reading “Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening”

Assessing the Dosimetric Links between Organ-At-Risk Delineation Variability and Treatment Planning Variability

The 2020 Joint AAPM | COMP Virtual Meetinghttps://w3.aapm.org/meetings/2020AM/programInfo/programAbs.php?sid=8490&aid=52949