FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3157684158> ?p ?o ?g. }

• W3157684158 endingPage "4854" @default.
• W3157684158 startingPage "4840" @default.
• W3157684158 abstract "Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results than conventional two-dimensional (2D) processing. However, deepening and widening 3D networks increases training difficulty significantly due to the large number of parameters and small number of training samples. Thus, we propose a 3D convolutional neural network (CNN) for SR of medical volumetric data called ParallelNet using parallel connections. We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters. As a result, the model thoroughly learns more feature maps with larger receptive fields. In addition, to further improve accuracy, we present an efficient version of ParallelNet (called VolumeNet), which reduces the number of parameters and deepens ParallelNet using a proposed lightweight building block module called the Queue module. Unlike most lightweight CNNs based on depthwise convolutions, the Queue module is primarily constructed using separable 2D cross-channel convolutions. As a result, the number of network parameters and computational complexity can be reduced significantly while maintaining accuracy due to full channel fusion. Experimental results demonstrate that the proposed VolumeNet significantly reduces the number of model parameters and achieves high precision results compared to state-of-the-art methods." @default.
• W3157684158 created "2021-05-10" @default.
• W3157684158 creator A5010150695 @default.
• W3157684158 creator A5010434486 @default.
• W3157684158 creator A5034166765 @default.
• W3157684158 creator A5057181928 @default.
• W3157684158 creator A5071445293 @default.
• W3157684158 creator A5090814258 @default.
• W3157684158 date "2021-01-01" @default.
• W3157684158 modified "2023-10-08" @default.
• W3157684158 title "VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data" @default.
• W3157684158 cites W1885185971 @default.
• W3157684158 cites W2024729467 @default.
• W3157684158 cites W2097117768 @default.
• W3157684158 cites W2121775913 @default.
• W3157684158 cites W2133665775 @default.
• W3157684158 cites W2194775991 @default.
• W3157684158 cites W2214802144 @default.
• W3157684158 cites W2242218935 @default.
• W3157684158 cites W2476548250 @default.
• W3157684158 cites W2531409750 @default.
• W3157684158 cites W2549139847 @default.
• W3157684158 cites W2551161082 @default.
• W3157684158 cites W2562637781 @default.
• W3157684158 cites W2607041014 @default.
• W3157684158 cites W2709402577 @default.
• W3157684158 cites W2741196023 @default.
• W3157684158 cites W2747675701 @default.
• W3157684158 cites W2747898905 @default.
• W3157684158 cites W2776107444 @default.
• W3157684158 cites W2780544323 @default.
• W3157684158 cites W2795024892 @default.
• W3157684158 cites W2799120945 @default.
• W3157684158 cites W2807184855 @default.
• W3157684158 cites W2895598217 @default.
• W3157684158 cites W2901354392 @default.
• W3157684158 cites W2912226037 @default.
• W3157684158 cites W2942080485 @default.
• W3157684158 cites W2953977469 @default.
• W3157684158 cites W2963125010 @default.
• W3157684158 cites W2963155035 @default.
• W3157684158 cites W2963163009 @default.
• W3157684158 cites W2963182372 @default.
• W3157684158 cites W2963186101 @default.
• W3157684158 cites W2963372104 @default.
• W3157684158 cites W2963419583 @default.
• W3157684158 cites W2963446712 @default.
• W3157684158 cites W2963470893 @default.
• W3157684158 cites W2963494934 @default.
• W3157684158 cites W2963578539 @default.
• W3157684158 cites W2963645458 @default.
• W3157684158 cites W2963671574 @default.
• W3157684158 cites W2963729050 @default.
• W3157684158 cites W2963844898 @default.
• W3157684158 cites W2964042923 @default.
• W3157684158 cites W2964101377 @default.
• W3157684158 cites W2964125708 @default.
• W3157684158 cites W2964277374 @default.
• W3157684158 cites W2964297772 @default.
• W3157684158 cites W2964349401 @default.
• W3157684158 cites W2980671251 @default.
• W3157684158 cites W3008234019 @default.
• W3157684158 cites W3013529009 @default.
• W3157684158 cites W3040495389 @default.
• W3157684158 cites W3098848838 @default.
• W3157684158 cites W3100855932 @default.
• W3157684158 cites W3101162162 @default.
• W3157684158 cites W3102467893 @default.
• W3157684158 cites W3106295246 @default.
• W3157684158 doi "https://doi.org/10.1109/tip.2021.3076285" @default.
• W3157684158 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33945478" @default.
• W3157684158 hasPublicationYear "2021" @default.
• W3157684158 type Work @default.
• W3157684158 sameAs 3157684158 @default.
• W3157684158 citedByCount "20" @default.
• W3157684158 countsByYear W31576841582021 @default.
• W3157684158 countsByYear W31576841582022 @default.
• W3157684158 countsByYear W31576841582023 @default.
• W3157684158 crossrefType "journal-article" @default.
• W3157684158 hasAuthorship W3157684158A5010150695 @default.
• W3157684158 hasAuthorship W3157684158A5010434486 @default.
• W3157684158 hasAuthorship W3157684158A5034166765 @default.
• W3157684158 hasAuthorship W3157684158A5057181928 @default.
• W3157684158 hasAuthorship W3157684158A5071445293 @default.
• W3157684158 hasAuthorship W3157684158A5090814258 @default.
• W3157684158 hasBestOaLocation W31576841582 @default.
• W3157684158 hasConcept C108583219 @default.
• W3157684158 hasConcept C11413529 @default.
• W3157684158 hasConcept C127162648 @default.
• W3157684158 hasConcept C134306372 @default.
• W3157684158 hasConcept C138885662 @default.
• W3157684158 hasConcept C153180895 @default.
• W3157684158 hasConcept C154945302 @default.
• W3157684158 hasConcept C160403385 @default.
• W3157684158 hasConcept C199360897 @default.
• W3157684158 hasConcept C202444582 @default.
• W3157684158 hasConcept C205372480 @default.
• W3157684158 hasConcept C2524010 @default.

• next