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Abstract #0762

Deep Learning for the Ovarian Lesion Localization and Discrimination Between Borderline Tumors and Cancers in MR Imaging

Yida Wang1, YinQiao Yi1, Haijie Wang1, Changan Chen2, Yingfang Wang2, Guofu Zhang2, He Zhang2, and Guang Yang1
1East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China, 2Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China

We proposed a deep learning (DL) approach to segment ovarian lesion and differentiate ovarian malignant from borderline tumors in MR Imaging. Firstly, we used U-net++ with deep supervision to automatically define lesion region on conventional MRI; secondly, the segmented ovarian masses regions were classified with an SE-ResNet model. We compared the performance of classification model with those of radiologist’. The results showed the trained DL network model could help to identify and categorize ovarian masses with a high accuracy from MR images.

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