Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis.

ABSTRACT: The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segment brain tumors and to classify different categories of tumors from different MRI modalities. However, these networks are often black-box models and do not provide any evidence regarding the process they take to perform this task. Increasing transparency and interpretability of such deep learning techniques is necessary for the complete integration of such methods into medical practice. In this paper, we explore various techniques to explain the functional organization of brain tumor segmentation models and to extract visualizations of interna

SUBMITTER: Natekar P 

PROVIDER: S-EPMC7025464 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature