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A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation. (arXiv:2203.00628v2 [q-bio.QM] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Purpose: To develop a neural ordinary differential equation (ODE) model for
visualizing deep neural network (DNN) behavior during multi-parametric MRI
(mp-MRI) based glioma segmentation as a method to enhance deep learning
explainability. Methods: By hypothesizing that deep feature extraction can be
modeled as a spatiotemporally continuous process, we designed a novel deep
learning model, neural ODE, in which deep feature extraction was governed by an
ODE without explicit expression. The dynamics of 1) MR images after
interactions with DNN and …
arxiv bio deep neural network equation network neural network ordinary segmentation