Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence. (arXiv:2206.03247v1 [eess.IV])

Accurate diagnosis and prognosis of Alzheimer's disease are crucial for
developing new therapies and reducing the associated costs. Recently, with the
advances of convolutional neural networks, deep learning methods have been
proposed to automate these two tasks using structural MRI. However, these
methods often suffer from a lack of interpretability and generalization and
have limited prognosis performance. In this paper, we propose a novel deep
framework designed to overcome these limitations. Our pipeline consists of two
stages. In the first …

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