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Abstract #3199

Adaptive convolution kernels for breast tumor segmentation in multi-parametric MR images

Cheng Li1, Hui Sun1, Zhenzhen Xue1, Xin Liu1, Hairong Zheng1, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China


Multi-parametric magnetic resonance imaging (mpMRI) provides high sensitivity and specificity for breast cancer diagnosis. Accurate breast tumor segmentation in mpMRI can help physicians achieve better clinical managements. Existing deep learning models have presented promising performances. However, the effective exploitation and fusion of information provided in mpMRI still need further investigation. In this study, we propose a convolutional neural network (CNN) with adaptive convolution kernels (AdaCNN) to automatically extract and absorb the useful information from multiple MRI sequences. Extensive experiments are conducted, and the proposed method can generate better breast tumor segmentation results than those obtained by CNNs with normal convolutions.

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