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”

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”