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

Frank Christiansen(Science for Life Laboratory), E. Epstein(Karolinska Institutet), Måns Åkerlund(Science for Life Laboratory), Kevin Smith(Karolinska Institutet), Erica Smedberg(Harvard University Press), Elise Epstein(Karolinska Institutet)

Ultrasound in Obstetrics and Gynecology

November 3, 2020

10.1002/uog.23530

Cited by 146

Related Papers

Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: a consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group

|Ultrasound in Obstetrics and Gynecology|2015|726

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

|BMJ|2014|500

Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group

|BMJ|2010|473

Clinicopathological and molecular characterisation of ‘multiple‐classifier’ endometrial carcinomas

|The Journal of Pathology|2019|401

Interpretation of somatic <i>POLE</i> mutations in endometrial carcinoma

|The Journal of Pathology|2019|389