A schematic diagram presents 2 steps of convolutional network training. Step 1 is random sampling, where patches are separated from scanned images to separate A D and N C. Step 2 is generating a probability map using patches.

Fig. 23

Randomly selected samples of T1-weighted full MRI volumes are used as input to learn the Alzheimer’s disease status at the individual level (Step 1). The application of the model to whole images leads to the generation of participant-specific disease probability maps of the brain (Step 2). (Adapted from Brain: A Journal of Neurology, 143, [46], 2020, with permission of Oxford University Press)

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

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Machine Learning for Brain Disorders [Internet].
Colliot O, editor.
New York, NY: Humana; 2023.
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