Observing deep radiomics for the classification of glioma grades
نویسندگان
چکیده
Abstract Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, technical challenge lies in the difficulty of determining which types internal representation are associated with specific task, feature vectors vary dynamically according to individual inputs. Here, based on magnetic resonance imaging (MRI) gliomas, we propose novel extract shareable set that encode various parts tumor phenotypes. By applying vector quantization latent representations, features extracted by an encoder replaced fixed vectors. Hence, be used downstream tasks as markers, call deep radiomics. Using radiomics, classifier established using logistic regression predict glioma grade 90% accuracy. We also devise algorithm visualize region encoded each vector, and demonstrate classification model preferentially relies presence or absence contrast enhancement regions. Our proposal provides data-driven approach enhance understanding appearance gliomas.
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2021
ISSN: ['2045-2322']
DOI: https://doi.org/10.1038/s41598-021-90555-2