Cargando…
Deep Feature Selection and Causal Analysis of Alzheimer’s Disease
Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer’s disease (AD). However, it is also well known that DCNNs are “black box...
Autores principales: | Liu, Yuanyuan, Li, Zhouxuan, Ge, Qiyang, Lin, Nan, Xiong, Momiao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872503/ https://www.ncbi.nlm.nih.gov/pubmed/31802999 http://dx.doi.org/10.3389/fnins.2019.01198 |
Ejemplares similares
-
Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection
por: Ge, Qiyang, et al.
Publicado: (2020) -
Causal Genomic and Epigenomic Network Analysis emerges as a New Generation of Genetic Studies of Complex Diseases
por: Xiong, Momiao
Publicado: (2015) -
Evaluation of Feature Selection for Alzheimer’s Disease Diagnosis
por: Gu, Feng, et al.
Publicado: (2022) -
Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis
por: Jiao, Rong, et al.
Publicado: (2018) -
Application of Causal Inference to Genomic Analysis: Advances in Methodology
por: Hu, Pengfei, et al.
Publicado: (2018)