MRI-based Ovarian Lesion Classification via a Foundation Segmentation Model and Multimodal Analysis: A Multicenter Study.

待确认 20 由 lmy 发布于 2026/1/24 14:47:46

DOI:10.1148/radiol.243412
作者：Wen-Chi Hsu,Yuli Wang,Yu-Fu Wu,Ruohua Chen,Shadi Afyouni,Jhehong Liu,Somasundaram Vin,Victoria Shi,Maliha Imami,Jill S Chotiyanonta,Ghazal Zandieh,Yeyu Cai,Jeffrey P Leal,Kenichi Oishi,Atif Zaheer,Rob
文献类型：期刊论文
补充材料：只需要正文

其它数据库

Abstract：Background Artificial intelligence may enhance diagnostic accuracy in classifying ovarian lesions on MRI scans; however, its applicability across diverse datasets is uncertain. Purpose To de

应助信息

等待求助人确认

求助人未确认前，本求助不能再次应助。36小时后系统将自动确认应助成功。本求助将自动关闭。

8小时前

Book学术机器人

06ee3a78889c11f083c934735aa3bc31.pdf

8小时前

Book学术机器人

2026/1/24 14:47:47 Book学术AI机器人已找到文献并上传

2026/1/24 14:47:46 Book学术AI机器人收到请求，开始寻找文献

2026/1/24 14:47:46 已向机器人发送请求

8小时前

lmy 发布求助

上一个求助:其它数据库Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary 下一个求助:ElsevierShort and long-term effects of a self-determination theory-based physical activity intervention on basic psychological needs and their mediating role in adults with depressive symptoms