Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study.

ABSTRACT:

Background

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening.

Methods

Between June 2020 and May 2022, the OvaMTA model was trained, validated

SUBMITTER: Dai WL 

PROVIDER: S-EPMC11617315 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature