Explainable Deep CNNs for MRI-Based Diagnosis of Alzheimer's Disease

Nigri, Eduardo; Ziviani, Nívio; Cappabianco, Fábio A. M.; Antunes, Augusto; Veloso, Adriano

doi:10.48550/arxiv.2004.12204

Cited by 1 publication

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“…Monitoring model output with perturbations in the input is another method to interpret neural network function. An example of this approach is the swap test proposed by Nigri et al [362], where patches of the image of interest are replaced by patches from reference images of an alternative class. The hippocampal region showed the highest impact on model predictions for the swap test.…”

Section: Path To Interpretation Of Deep Learning Modelsmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Section: Path To Interpretation Of Deep Learning Modelsmentioning

confidence: 99%