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”
Tag Archives: Computer Aided Detection
Artificial Intelligence in Radiation Oncology
Image processing in lung cancer screening and treatment
Invited talk in GIST, Nov 2014
Computer Aided Detection of Pulmonary Nodules in CT Scans
Pulmonary Nodule Detection using Voxel Classification in Lung CT images (Korean)
Lung structure segmentation and nodule detection based on 3D block analysis in CT image (Korean)
Computer-aided Detection of Pulmonary Nodules using Genetic Programming (Korean)
Computer aided detection of pulmonary nodules using genetic programming
Computer-aided Detection of Pulmonary Nodules using Genetic Programming
2010 IEEE ICIP
Image Analysis and Nodule Detection System in 3D Lung CT Images using Insight toolkit (Korean)
Invited talk in CNUH, Apr 2014