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”

Interpretable Spiculation Quantification for Lung Cancer Screening

UKC2018 Aug 4, 2018 MSKCC Postdoctoral Research Symposium Sep 28, 2018 Interpretable Spiculation Quantification for Lung Cancer Screening. https://t.co/QucUlu2QVE pic.twitter.com/FaOukTeIPJ — arxiv (@arxiv_org) August 29, 2018 Presented at MICCAI ShapeMI Workshop https://shapemi.github.io/program/  

Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Adenocarcinoma

2016 ASTRO annual meeting This poster has been selected for the ARRO poster walk (6 out of 250 physics posters). http://onlinelibrary.wiley.com/doi/10.1118/1.4963213/full