3D Dense U-Net-ASPP for MRI Imaging Segmentation of Breast Tumors

Breast cancer is one of the most common cancers among women in our country. Magnetic resonance imaging (MRI) has become a widely adopted medical imaging technology in the healthcare industry. It can help medical professionals accurately determine the size and scope of breast cancer. This study proposes a 3D Dense U-Net-ASPP model using a private dataset. The model addresses the issue of large data volume in image segmentation preprocessing and reduces the problem of feature loss by combining Atrous Spatial Pyramid Pooling (ASPP) and Attention Gate. Furthermore, the model’s performance is improved by adjusting the loss function through a combination of Focal Loss and Dice Loss.