Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204385590> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3204385590 abstract "The application of deep learning in the field of diffusion imaging is becoming increasingly popular. However, correlations of acquired adjacent gradient directions are often ignored. To make use of this information in a neural network, a spherical convolution is necessary. This work evaluates three different ways to include spherical information: 2D projection, local spherical convolution and Fourier space transform. For comparison, all models are designed to have a similar amount of trainable parameters as well as the same network architecture, and are evaluated by considering the example of signal augmentation. Overall, all models achieved comparable good results, improving the reconstruction performance, compared to a reconstruction without augmentation, by (approx )30% for the fractional anisotropy, (approx )50% for the mean diffusivity, (approx )70% for the mean signal kurtosis and (approx )5% for the diffusion signal itself. Particularly, in comparison to a regular neural network that does not implement a spherical convolution, the average performance for all models that implement a sperical convolution increases slightly for all evaluated measures, where the local spherical convolution shows the most favorable results." @default.
- W3204385590 created "2021-10-11" @default.
- W3204385590 creator A5052311060 @default.
- W3204385590 creator A5064747056 @default.
- W3204385590 date "2021-01-01" @default.
- W3204385590 modified "2023-10-14" @default.
- W3204385590 title "Enhancing Diffusion Signal Augmentation Using Spherical Convolutions" @default.
- W3204385590 cites W1530581289 @default.
- W3204385590 cites W1901129140 @default.
- W3204385590 cites W1964802316 @default.
- W3204385590 cites W1984453610 @default.
- W3204385590 cites W2001611992 @default.
- W3204385590 cites W2024729467 @default.
- W3204385590 cites W2044904021 @default.
- W3204385590 cites W2057123140 @default.
- W3204385590 cites W2062791478 @default.
- W3204385590 cites W2111508341 @default.
- W3204385590 cites W2116823839 @default.
- W3204385590 cites W2128207744 @default.
- W3204385590 cites W2129841144 @default.
- W3204385590 cites W2136573752 @default.
- W3204385590 cites W2147133578 @default.
- W3204385590 cites W2194775991 @default.
- W3204385590 cites W2328247767 @default.
- W3204385590 cites W2409942661 @default.
- W3204385590 cites W2558748708 @default.
- W3204385590 cites W2612025826 @default.
- W3204385590 cites W2908664706 @default.
- W3204385590 cites W2963351448 @default.
- W3204385590 cites W2963717741 @default.
- W3204385590 doi "https://doi.org/10.1007/978-3-030-73018-5_15" @default.
- W3204385590 hasPublicationYear "2021" @default.
- W3204385590 type Work @default.
- W3204385590 sameAs 3204385590 @default.
- W3204385590 citedByCount "0" @default.
- W3204385590 crossrefType "book-chapter" @default.
- W3204385590 hasAuthorship W3204385590A5052311060 @default.
- W3204385590 hasAuthorship W3204385590A5064747056 @default.
- W3204385590 hasConcept C102519508 @default.
- W3204385590 hasConcept C105795698 @default.
- W3204385590 hasConcept C111919701 @default.
- W3204385590 hasConcept C11413529 @default.
- W3204385590 hasConcept C118615104 @default.
- W3204385590 hasConcept C121332964 @default.
- W3204385590 hasConcept C134306372 @default.
- W3204385590 hasConcept C154945302 @default.
- W3204385590 hasConcept C166963901 @default.
- W3204385590 hasConcept C199360897 @default.
- W3204385590 hasConcept C2777894999 @default.
- W3204385590 hasConcept C2779843651 @default.
- W3204385590 hasConcept C33923547 @default.
- W3204385590 hasConcept C41008148 @default.
- W3204385590 hasConcept C45347329 @default.
- W3204385590 hasConcept C50644808 @default.
- W3204385590 hasConcept C69357855 @default.
- W3204385590 hasConcept C74193536 @default.
- W3204385590 hasConcept C97355855 @default.
- W3204385590 hasConceptScore W3204385590C102519508 @default.
- W3204385590 hasConceptScore W3204385590C105795698 @default.
- W3204385590 hasConceptScore W3204385590C111919701 @default.
- W3204385590 hasConceptScore W3204385590C11413529 @default.
- W3204385590 hasConceptScore W3204385590C118615104 @default.
- W3204385590 hasConceptScore W3204385590C121332964 @default.
- W3204385590 hasConceptScore W3204385590C134306372 @default.
- W3204385590 hasConceptScore W3204385590C154945302 @default.
- W3204385590 hasConceptScore W3204385590C166963901 @default.
- W3204385590 hasConceptScore W3204385590C199360897 @default.
- W3204385590 hasConceptScore W3204385590C2777894999 @default.
- W3204385590 hasConceptScore W3204385590C2779843651 @default.
- W3204385590 hasConceptScore W3204385590C33923547 @default.
- W3204385590 hasConceptScore W3204385590C41008148 @default.
- W3204385590 hasConceptScore W3204385590C45347329 @default.
- W3204385590 hasConceptScore W3204385590C50644808 @default.
- W3204385590 hasConceptScore W3204385590C69357855 @default.
- W3204385590 hasConceptScore W3204385590C74193536 @default.
- W3204385590 hasConceptScore W3204385590C97355855 @default.
- W3204385590 hasLocation W32043855901 @default.
- W3204385590 hasOpenAccess W3204385590 @default.
- W3204385590 hasPrimaryLocation W32043855901 @default.
- W3204385590 hasRelatedWork W10367256 @default.
- W3204385590 hasRelatedWork W12139544 @default.
- W3204385590 hasRelatedWork W5571305 @default.
- W3204385590 hasRelatedWork W7036924 @default.
- W3204385590 hasRelatedWork W7454294 @default.
- W3204385590 hasRelatedWork W7867045 @default.
- W3204385590 hasRelatedWork W910914 @default.
- W3204385590 hasRelatedWork W9575191 @default.
- W3204385590 hasRelatedWork W10467723 @default.
- W3204385590 hasRelatedWork W7727475 @default.
- W3204385590 isParatext "false" @default.
- W3204385590 isRetracted "false" @default.
- W3204385590 magId "3204385590" @default.
- W3204385590 workType "book-chapter" @default.