Name
Title | ||
|---|---|---|
Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders. |
Abstract | ||
|---|---|---|
•Introduces a new method for single slice Alzheimer's disease identification merging unsupervised and supervised learning.•Proposes a new supervised autoencoder architecture to learn label-enriched visual representations and classification models.•Presents a novel image analysis approach to identify Alzheimer's disease relevant regions from autoencoder reconstructions.•Evaluates the association between identified relevant regions and current medical knowledge, finding them markedly coherent. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compbiomed.2019.103527 | Computers in Biology and Medicine |
Keywords | DocType | Volume |
Alzheimer disease,Supervised autoencoder,Supervised switching autoencoder,Convolutional neural networks,Representation learning,Magnetic resonance imaging | Journal | 116 |
ISSN | Citations | PageRank |
0010-4825 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Mendoza-Léon | 1 | 0 | 0.34 |
| John Puentes | 2 | 33 | 9.23 |
| Luis Felipe Uriza | 3 | 0 | 0.34 |
| Marcela Hernández Hoyos | 4 | 228 | 15.91 |