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”
Tag Archives: Image
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”
Artificial Intelligence in Radiation Oncology
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”
Radiomics in Lung Cancer
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/
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease
2017 ASTRO annual meeting http://www.redjournal.org/article/S0360-3016(17)31540-7/fulltext
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
Current Projects – Sep 13, 2016
Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease
2016 AAPM annual meeting http://onlinelibrary.wiley.com/doi/10.1118/1.4955803/abstract
Qualia Radiomics 