Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312210588> ?p ?o ?g. }
- W4312210588 endingPage "105692" @default.
- W4312210588 startingPage "105692" @default.
- W4312210588 abstract "The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames." @default.
- W4312210588 created "2023-01-04" @default.
- W4312210588 creator A5009825595 @default.
- W4312210588 creator A5017431053 @default.
- W4312210588 creator A5025418165 @default.
- W4312210588 creator A5029741979 @default.
- W4312210588 creator A5039984374 @default.
- W4312210588 creator A5042657346 @default.
- W4312210588 creator A5048379858 @default.
- W4312210588 creator A5050214162 @default.
- W4312210588 creator A5051254881 @default.
- W4312210588 creator A5052787170 @default.
- W4312210588 creator A5055989083 @default.
- W4312210588 creator A5058579716 @default.
- W4312210588 creator A5068732609 @default.
- W4312210588 creator A5085332627 @default.
- W4312210588 creator A5088465882 @default.
- W4312210588 date "2023-01-01" @default.
- W4312210588 modified "2023-10-01" @default.
- W4312210588 title "Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis" @default.
- W4312210588 cites W1987960874 @default.
- W4312210588 cites W1991113411 @default.
- W4312210588 cites W1995875735 @default.
- W4312210588 cites W2003105607 @default.
- W4312210588 cites W2064752556 @default.
- W4312210588 cites W2067864218 @default.
- W4312210588 cites W2076063813 @default.
- W4312210588 cites W2131076848 @default.
- W4312210588 cites W2140639259 @default.
- W4312210588 cites W2154145988 @default.
- W4312210588 cites W2305392477 @default.
- W4312210588 cites W2509017967 @default.
- W4312210588 cites W2588463901 @default.
- W4312210588 cites W2611533960 @default.
- W4312210588 cites W2618530766 @default.
- W4312210588 cites W2731899572 @default.
- W4312210588 cites W2763615846 @default.
- W4312210588 cites W2774292910 @default.
- W4312210588 cites W2789956930 @default.
- W4312210588 cites W2803760365 @default.
- W4312210588 cites W2906295032 @default.
- W4312210588 cites W2906598409 @default.
- W4312210588 cites W2985003253 @default.
- W4312210588 cites W3015538848 @default.
- W4312210588 cites W3034855613 @default.
- W4312210588 cites W3046472267 @default.
- W4312210588 cites W3084209621 @default.
- W4312210588 cites W3098949126 @default.
- W4312210588 cites W3099878876 @default.
- W4312210588 cites W3119005666 @default.
- W4312210588 cites W3120863277 @default.
- W4312210588 cites W3128646645 @default.
- W4312210588 cites W3195836789 @default.
- W4312210588 cites W4206841660 @default.
- W4312210588 cites W4288064372 @default.
- W4312210588 doi "https://doi.org/10.1016/j.isci.2022.105692" @default.
- W4312210588 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36570770" @default.
- W4312210588 hasPublicationYear "2023" @default.
- W4312210588 type Work @default.
- W4312210588 citedByCount "2" @default.
- W4312210588 countsByYear W43122105882023 @default.
- W4312210588 crossrefType "journal-article" @default.
- W4312210588 hasAuthorship W4312210588A5009825595 @default.
- W4312210588 hasAuthorship W4312210588A5017431053 @default.
- W4312210588 hasAuthorship W4312210588A5025418165 @default.
- W4312210588 hasAuthorship W4312210588A5029741979 @default.
- W4312210588 hasAuthorship W4312210588A5039984374 @default.
- W4312210588 hasAuthorship W4312210588A5042657346 @default.
- W4312210588 hasAuthorship W4312210588A5048379858 @default.
- W4312210588 hasAuthorship W4312210588A5050214162 @default.
- W4312210588 hasAuthorship W4312210588A5051254881 @default.
- W4312210588 hasAuthorship W4312210588A5052787170 @default.
- W4312210588 hasAuthorship W4312210588A5055989083 @default.
- W4312210588 hasAuthorship W4312210588A5058579716 @default.
- W4312210588 hasAuthorship W4312210588A5068732609 @default.
- W4312210588 hasAuthorship W4312210588A5085332627 @default.
- W4312210588 hasAuthorship W4312210588A5088465882 @default.
- W4312210588 hasBestOaLocation W43122105881 @default.
- W4312210588 hasConcept C121608353 @default.
- W4312210588 hasConcept C126042441 @default.
- W4312210588 hasConcept C126322002 @default.
- W4312210588 hasConcept C126838900 @default.
- W4312210588 hasConcept C143753070 @default.
- W4312210588 hasConcept C154945302 @default.
- W4312210588 hasConcept C19527891 @default.
- W4312210588 hasConcept C2777423100 @default.
- W4312210588 hasConcept C2777432617 @default.
- W4312210588 hasConcept C2778491387 @default.
- W4312210588 hasConcept C2780472235 @default.
- W4312210588 hasConcept C41008148 @default.
- W4312210588 hasConcept C529618451 @default.
- W4312210588 hasConcept C530470458 @default.
- W4312210588 hasConcept C71924100 @default.
- W4312210588 hasConcept C76155785 @default.
- W4312210588 hasConceptScore W4312210588C121608353 @default.
- W4312210588 hasConceptScore W4312210588C126042441 @default.
- W4312210588 hasConceptScore W4312210588C126322002 @default.
- W4312210588 hasConceptScore W4312210588C126838900 @default.