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: Spiculation
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”
Quantitative Cancer Image Analysis
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/
Qualia Radiomics 