Cargando…
Interpretable brain disease classification and relevance-guided deep learning
Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks’ decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial...
Autores principales: | Tinauer, Christian, Heber, Stefan, Pirpamer, Lukas, Damulina, Anna, Schmidt, Reinhold, Stollberger, Rudolf, Ropele, Stefan, Langkammer, Christian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691637/ https://www.ncbi.nlm.nih.gov/pubmed/36424437 http://dx.doi.org/10.1038/s41598-022-24541-7 |
Ejemplares similares
-
Periventricular magnetisation transfer abnormalities in early multiple sclerosis
por: Pirpamer, Lukas, et al.
Publicado: (2022) -
Assessment and correction of macroscopic field variations in 2D spoiled gradient‐echo sequences
por: Soellradl, Martin, et al.
Publicado: (2019) -
Heritability of R2* iron in the basal ganglia and cortex
por: Hofer, Edith, et al.
Publicado: (2022) -
Multimodal assessment of white matter tracts in amyotrophic lateral sclerosis
por: Borsodi, Florian, et al.
Publicado: (2017) -
Adaptive slice‐specific z‐shimming for 2D spoiled gradient‐echo sequences
por: Soellradl, Martin, et al.
Publicado: (2020)