Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.
Saved in:
| Title: | Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. |
|---|---|
| Authors: | Christiansen F; School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden., Epstein EL; School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden., Smedberg E; Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden., Åkerlund M; Harvard Extension School, Harvard University, Cambridge, MA, USA., Smith K; Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden., Epstein E; Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden. |
| Source: | Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology [Ultrasound Obstet Gynecol] 2021 Jan; Vol. 57 (1), pp. 155-163. |
| Publication Type: | Journal Article; Validation Study |
| Journal Info: | Publisher: John Wiley & Sons, Ltd Country of Publication: England NLM ID: 9108340 Publication Model: Print Cited Medium: Internet ISSN: 1469-0705 (Electronic) Linking ISSN: 09607692 NLM ISO Abbreviation: Ultrasound Obstet Gynecol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 33142359 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Christiansen+F%22">Christiansen F</searchLink>; School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Epstein+EL%22">Epstein EL</searchLink>; School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Smedberg+E%22">Smedberg E</searchLink>; Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Åkerlund+M%22">Åkerlund M</searchLink>; Harvard Extension School, Harvard University, Cambridge, MA, USA.<br /><searchLink fieldCode="AU" term="%22Smith+K%22">Smith K</searchLink>; Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Epstein+E%22">Epstein E</searchLink>; Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229108340%22">Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology</searchLink> [Ultrasound Obstet Gynecol] 2021 Jan; Vol. 57 (1), pp. 155-163. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Validation Study – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22John+Wiley+%26+Sons%2C+Ltd%22">John Wiley & Sons, Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9108340 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1469-0705 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209607692%22">09607692 </searchLink><i>NLM ISO Abbreviation: </i>Ultrasound Obstet Gynecol <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=33142359 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/uog.23530 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 155 Titles: – TitleFull: Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Christiansen F – PersonEntity: Name: NameFull: Epstein EL – PersonEntity: Name: NameFull: Smedberg E – PersonEntity: Name: NameFull: Åkerlund M – PersonEntity: Name: NameFull: Smith K – PersonEntity: Name: NameFull: Epstein E IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2021 Jan Type: published Y: 2021 Identifiers: – Type: issn-electronic Value: 1469-0705 Numbering: – Type: volume Value: 57 – Type: issue Value: 1 Titles: – TitleFull: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology Type: main |
| ResultId | 1 |
