Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309014742> ?p ?o ?g. }
- W4309014742 endingPage "101666" @default.
- W4309014742 startingPage "101666" @default.
- W4309014742 abstract "•Neural network approaches show the most potential for automated image analysis of thecervical spine.•Fully automatic convolutional neural network (CNN) models are promising Deep Learning methods for segmentation.•In cervical spine analysis, the biomechanical features are most often studied using finiteelement models.•The application of artificial neural networks and support vector machine models looks promising for classification purposes.•This article provides an overview of the methods for research on computer aided imaging diagnostics of the cervical spine." @default.
- W4309014742 created "2022-11-20" @default.
- W4309014742 creator A5015537991 @default.
- W4309014742 creator A5023654831 @default.
- W4309014742 creator A5025477214 @default.
- W4309014742 creator A5025788902 @default.
- W4309014742 creator A5043612857 @default.
- W4309014742 creator A5047769635 @default.
- W4309014742 creator A5060733110 @default.
- W4309014742 date "2022-01-01" @default.
- W4309014742 modified "2023-09-27" @default.
- W4309014742 title "Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods" @default.
- W4309014742 cites W1519750838 @default.
- W4309014742 cites W1542060247 @default.
- W4309014742 cites W184565980 @default.
- W4309014742 cites W1891410852 @default.
- W4309014742 cites W1950915640 @default.
- W4309014742 cites W1964697478 @default.
- W4309014742 cites W1975607565 @default.
- W4309014742 cites W1976180530 @default.
- W4309014742 cites W1979064019 @default.
- W4309014742 cites W1982846171 @default.
- W4309014742 cites W1990975940 @default.
- W4309014742 cites W2022251943 @default.
- W4309014742 cites W2053980954 @default.
- W4309014742 cites W2055233911 @default.
- W4309014742 cites W2069761333 @default.
- W4309014742 cites W2092482885 @default.
- W4309014742 cites W2097583462 @default.
- W4309014742 cites W2100735876 @default.
- W4309014742 cites W2101087183 @default.
- W4309014742 cites W2105850664 @default.
- W4309014742 cites W2119520868 @default.
- W4309014742 cites W2156098321 @default.
- W4309014742 cites W2157342597 @default.
- W4309014742 cites W2164715814 @default.
- W4309014742 cites W2167437237 @default.
- W4309014742 cites W2321283863 @default.
- W4309014742 cites W2334152667 @default.
- W4309014742 cites W246286872 @default.
- W4309014742 cites W2533352651 @default.
- W4309014742 cites W2558093628 @default.
- W4309014742 cites W2562251009 @default.
- W4309014742 cites W2578452911 @default.
- W4309014742 cites W2581082771 @default.
- W4309014742 cites W2615355698 @default.
- W4309014742 cites W2782962239 @default.
- W4309014742 cites W2799277348 @default.
- W4309014742 cites W2805987271 @default.
- W4309014742 cites W2885824038 @default.
- W4309014742 cites W2910109268 @default.
- W4309014742 cites W2914201535 @default.
- W4309014742 cites W2923067190 @default.
- W4309014742 cites W2938034334 @default.
- W4309014742 cites W2940487144 @default.
- W4309014742 cites W2945421678 @default.
- W4309014742 cites W2947652778 @default.
- W4309014742 cites W2957485116 @default.
- W4309014742 cites W2963202012 @default.
- W4309014742 cites W2964324957 @default.
- W4309014742 cites W2975687978 @default.
- W4309014742 cites W2978352565 @default.
- W4309014742 cites W2979414332 @default.
- W4309014742 cites W2989492337 @default.
- W4309014742 cites W2995609771 @default.
- W4309014742 cites W3010245316 @default.
- W4309014742 cites W3092732947 @default.
- W4309014742 cites W3092988518 @default.
- W4309014742 cites W3095138331 @default.
- W4309014742 cites W3109354682 @default.
- W4309014742 cites W3119075307 @default.
- W4309014742 cites W3197433610 @default.
- W4309014742 cites W4294215472 @default.
- W4309014742 cites W2071602987 @default.
- W4309014742 doi "https://doi.org/10.1016/j.bas.2022.101666" @default.
- W4309014742 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36506292" @default.
- W4309014742 hasPublicationYear "2022" @default.
- W4309014742 type Work @default.
- W4309014742 citedByCount "0" @default.
- W4309014742 crossrefType "journal-article" @default.
- W4309014742 hasAuthorship W4309014742A5015537991 @default.
- W4309014742 hasAuthorship W4309014742A5023654831 @default.
- W4309014742 hasAuthorship W4309014742A5025477214 @default.
- W4309014742 hasAuthorship W4309014742A5025788902 @default.
- W4309014742 hasAuthorship W4309014742A5043612857 @default.
- W4309014742 hasAuthorship W4309014742A5047769635 @default.
- W4309014742 hasAuthorship W4309014742A5060733110 @default.
- W4309014742 hasBestOaLocation W43090147421 @default.
- W4309014742 hasConcept C108583219 @default.
- W4309014742 hasConcept C119857082 @default.
- W4309014742 hasConcept C12267149 @default.
- W4309014742 hasConcept C141071460 @default.
- W4309014742 hasConcept C153180895 @default.
- W4309014742 hasConcept C154945302 @default.
- W4309014742 hasConcept C2985379065 @default.
- W4309014742 hasConcept C41008148 @default.
- W4309014742 hasConcept C50644808 @default.
- W4309014742 hasConcept C71924100 @default.