A framework for DaniNet. Input passes through an encoder, generator, and then the framework identifies whether the image is real or synthetic. The framework then calculates several types of loss, including reconstruction loss, voxel loss, regional loss, and the profile weight loss function.

Fig. 25

Pipeline used for training the proposed DaniNet framework that aims to learn a longitudinal model of the progression of Alzheimer’s disease. (Adapted from [47] (CC BY 4.0))

From: Chapter 22, Interpretability of Machine Learning Methods Applied to Neuroimaging

Cover of Machine Learning for Brain Disorders
Machine Learning for Brain Disorders [Internet].
Colliot O, editor.
New York, NY: Humana; 2023.
Copyright 2023, The Author(s)

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