Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment-Reference-Cited by-同舟云学术

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

Published:2021-01 Issue:1 Volume:57 Page:155-163
ISSN:0960-7692
Container-title:Ultrasound in Obstetrics & Gynecology
language:en
Short-container-title:Ultrasound Obstet Gynecol

Author:

Christiansen F.¹,Epstein E. L.¹,Smedberg E.²,Åkerlund M.³,Smith K.⁴,Epstein E.²^ORCID

Affiliation:

1. School of Engineering Sciences, KTH Royal Institute of Technology Stockholm Sweden

2. Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset Stockholm Sweden

3. Harvard Extension School Harvard University Cambridge MA USA

4. Science for Life Laboratory, School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Funder

Radiumhemmets Forskningsfonder

Publisher

Wiley

Subject

Obstetrics and Gynecology,Radiology, Nuclear Medicine and imaging,Reproductive Medicine,General Medicine,Radiological and Ultrasound Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/uog.23530

Reference37 articles.

1. Epidemiology of epithelial ovarian cancer

2. Risk of epithelial ovarian cancer in asymptomatic women with ultrasound-detected ovarian masses: a prospective cohort study within the UK collaborative trial of ovarian cancer screening (UKCTOCS)

3. Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study

4. Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma

5. Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis

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