R. Miron, R. Albert, M. Breaban

A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction

Medical Imaging Analysis and Diagnostics Machine Learning Deep Learning Artificial Intelligence

Glioma is a type of heterogeneous tumor originating in the brain, characterized by the coexistence of multiple subregions with different phenotypic characteristics, which further determine heterogeneous profiles, likely to respond variably to treatment. Identifying spatial variations of gliomas is necessary for targeted therapy. The current paper proposes a neural network composed of heterogeneous building blocks to identify the different histologic sub-regions of gliomas in multi-parametric MRIs and further extracts radiomic features to estimate a patient’s prognosis. The model is evaluated on the BraTS 2020 dataset.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE