Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203313349> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W3203313349 endingPage "44" @default.
- W3203313349 startingPage "35" @default.
- W3203313349 abstract "Deep learning techniques have recently been adopted for accelerating dynamic MRI acquisitions. Yet, common frameworks for model training rely on availability of large sets of fully-sampled MRI data to construct a ground-truth for the network output. This heavy reliance is undesirable as it is challenging to collect such large datasets in many applications, and even impossible for high spatiotemporal-resolution protocols. In this paper, we introduce self-supervised training to deep neural architectures for dynamic reconstruction of cardiac MRI. We hypothesize that, in the absence of ground-truth data, elevating complexity in self-supervised models can instead constrain model performance due to the deficiencies in training data. To test this working hypothesis, we adopt self-supervised learning on recent state-of-the-art deep models for dynamic MRI, with varying degrees of model complexity. Comparison of supervised and self-supervised variants of deep reconstruction models reveals that compact models have a remarkable advantage in reliability against performance loss in self-supervised settings." @default.
- W3203313349 created "2021-10-11" @default.
- W3203313349 creator A5032375235 @default.
- W3203313349 creator A5041208811 @default.
- W3203313349 creator A5080738214 @default.
- W3203313349 date "2021-01-01" @default.
- W3203313349 modified "2023-10-16" @default.
- W3203313349 title "Self-supervised Dynamic MRI Reconstruction" @default.
- W3203313349 cites W1901129140 @default.
- W3203313349 cites W2019371831 @default.
- W3203313349 cites W2038694265 @default.
- W3203313349 cites W2056775112 @default.
- W3203313349 cites W2132122471 @default.
- W3203313349 cites W2442117232 @default.
- W3203313349 cites W2594014149 @default.
- W3203313349 cites W2891214351 @default.
- W3203313349 cites W2950936580 @default.
- W3203313349 cites W3019742258 @default.
- W3203313349 cites W3035596626 @default.
- W3203313349 cites W3039236647 @default.
- W3203313349 cites W3101847293 @default.
- W3203313349 cites W3103007448 @default.
- W3203313349 cites W3103618922 @default.
- W3203313349 cites W4226133625 @default.
- W3203313349 doi "https://doi.org/10.1007/978-3-030-88552-6_4" @default.
- W3203313349 hasPublicationYear "2021" @default.
- W3203313349 type Work @default.
- W3203313349 sameAs 3203313349 @default.
- W3203313349 citedByCount "4" @default.
- W3203313349 countsByYear W32033133492022 @default.
- W3203313349 countsByYear W32033133492023 @default.
- W3203313349 crossrefType "book-chapter" @default.
- W3203313349 hasAuthorship W3203313349A5032375235 @default.
- W3203313349 hasAuthorship W3203313349A5041208811 @default.
- W3203313349 hasAuthorship W3203313349A5080738214 @default.
- W3203313349 hasConcept C108583219 @default.
- W3203313349 hasConcept C119857082 @default.
- W3203313349 hasConcept C121332964 @default.
- W3203313349 hasConcept C136389625 @default.
- W3203313349 hasConcept C146849305 @default.
- W3203313349 hasConcept C154945302 @default.
- W3203313349 hasConcept C163258240 @default.
- W3203313349 hasConcept C199360897 @default.
- W3203313349 hasConcept C2780801425 @default.
- W3203313349 hasConcept C2984842247 @default.
- W3203313349 hasConcept C41008148 @default.
- W3203313349 hasConcept C43214815 @default.
- W3203313349 hasConcept C50644808 @default.
- W3203313349 hasConcept C62520636 @default.
- W3203313349 hasConceptScore W3203313349C108583219 @default.
- W3203313349 hasConceptScore W3203313349C119857082 @default.
- W3203313349 hasConceptScore W3203313349C121332964 @default.
- W3203313349 hasConceptScore W3203313349C136389625 @default.
- W3203313349 hasConceptScore W3203313349C146849305 @default.
- W3203313349 hasConceptScore W3203313349C154945302 @default.
- W3203313349 hasConceptScore W3203313349C163258240 @default.
- W3203313349 hasConceptScore W3203313349C199360897 @default.
- W3203313349 hasConceptScore W3203313349C2780801425 @default.
- W3203313349 hasConceptScore W3203313349C2984842247 @default.
- W3203313349 hasConceptScore W3203313349C41008148 @default.
- W3203313349 hasConceptScore W3203313349C43214815 @default.
- W3203313349 hasConceptScore W3203313349C50644808 @default.
- W3203313349 hasConceptScore W3203313349C62520636 @default.
- W3203313349 hasLocation W32033133491 @default.
- W3203313349 hasOpenAccess W3203313349 @default.
- W3203313349 hasPrimaryLocation W32033133491 @default.
- W3203313349 hasRelatedWork W1976205134 @default.
- W3203313349 hasRelatedWork W2366107444 @default.
- W3203313349 hasRelatedWork W2798269247 @default.
- W3203313349 hasRelatedWork W2799384463 @default.
- W3203313349 hasRelatedWork W3000197790 @default.
- W3203313349 hasRelatedWork W3193857078 @default.
- W3203313349 hasRelatedWork W3208304128 @default.
- W3203313349 hasRelatedWork W4310825149 @default.
- W3203313349 hasRelatedWork W4377865163 @default.
- W3203313349 hasRelatedWork W4386076228 @default.
- W3203313349 isParatext "false" @default.
- W3203313349 isRetracted "false" @default.
- W3203313349 magId "3203313349" @default.
- W3203313349 workType "book-chapter" @default.