Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study
Wen-Li Dai, Ying-Nan Wu, Ya-Ting Ling, Jing Zhao, Shuang Zhang, Zhao-Wen Gu, Li-Ping Gong, Man-Ning Zhu, Shuang Dong, Song-Cheng Xu, Lei Wu, Li-Tao Sun, De-Xing Kong · 2024
Read Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study by Wen-Li Dai, Ying-Nan Wu, Ya-Ting Ling, Jing Zhao, Shuang Zhang, Zhao-Wen Gu, Li-Ping Gong, Man-Ning Zhu, Shuang Dong, Song-Cheng Xu, Lei Wu, Li-Tao Sun, De-Xing Kong (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11617315.txt.
Description
Wen-Li Dai and co-authors build a deep learning system to diagnose ovarian masses from ultrasound in this 2024 paper. Ovarian cancer has the highest death rate among gynecologic cancers. Ultrasound is the first screen, but the images are complex. Reading them well takes long experience.
Their system, called OvaMTA, finds the ovaries, outlines masses, and classifies them in one pipeline. Training used 6,938 images from 21 hospitals, with a separate internal test set. Two outside hospital cohorts tested the system later, one with still images and one with videos. The goal is steady, expert level screening support across many sites. Results are reported for each stage of the pipeline.
Catalog record
| CREATOR | Wen-Li Dai, Ying-Nan Wu, Ya-Ting Ling, Jing Zhao, Shuang Zhang, Zhao-Wen Gu, Li-Ping Gong, Man-Ning Zhu, Shuang Dong, Song-Cheng Xu, Lei Wu, Li-Tao Sun, De-Xing Kong |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |