Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.
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| Title: | Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. |
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| 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 |
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