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Abstract #4522

Ensemble Multi-Path U-net Segmentation Algorithm for Breast Lesion based on Multi-Modality Image

Hang Yu1, Zichuan Xie2, Lizhi Xie3, Zhiheng Liu1, Lina Zhang4, Siyao Du4, Xiangjie Yin1, Chenyang Li1, Wenhong Jiang4, Yuru Guo1, and Zhongqi Kang4
1School of Aerospace Science and Technology, Xidian University, Xi'an, China, 2Guangzhou institute of technology, Xidian University, Guangzhou, China, 3GE Healthcare, Beijing, China, 4Department of Radiology, The First Hospital of China Medical University, Shenyang, China

Synopsis

Keywords: Breast, Machine Learning/Artificial Intelligence, Deep LearningThe multimodal MRI data is often ultilized for breast cancer analysis, and by now still difficult and inefficient to explored by segmentation algorithms. In this paper, we propose a MP-Unet based on U-net convolutional neural network, which can effectively ensemble multiple inputs of modal data and obtain accuracy segmentation results at the end. In MP-Unet, we reused some good quality modal data for training. The multiple MP-Unet models are further integrated based on Bagging algorithm to improve the segmentation accuracy of lesions. Experiments suggest that our proposed method has a huge performance improvement.

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