关键词: 关键词: 医学图像分割, 迁移学习, 多角度特征融合, 自适应组合赋权, Transformer

Abstract: In view of the lack of traditional image segmentation algorithms to guide convolutional neural network (CNN) for segmentation in the current field of medical image segmentation, a medical image segmentation network with content-guided multi-angle feature fusion (CGMAFF-Net) was proposed. First, grayscale images and Otsu threshold segmentation images were used to generate lesion region guidance maps through a transformer-based micro U-shaped feature extraction module, and adaptive combination weighting (ACW) was used to weight them onto the original medical images for initial guidance. Then, the residual neural network (ResNet) was employed to extract downsampled features from the weighted medical images, and a multi-angle feature fusion (MAFF) module was used to fuse feature maps at 1/16 and 1/8 scales. Finally, reverse attention (RA) was applied to upsample and gradually restore the feature map size to predict key lesion regions. Experiments on the CVC-ClinicDB, Kvasir-SEG, and ISIC 2018 datasets demonstrated that, compared to the best-performing segmentation model MSRAformer, the mean intersection over union (mIoU) increased by 0.97, 0.78, and 0.11 percentage points, respectively. Compared to the classic network UNet, mIoU increased by 2.66, 8.94, and 1.69 percentage points, respectively, fully proving the effectiveness and advancement of CGMAFF-Net.

Key words: Keywords: medical image segmentation, transfer learning, multi-angle feature fusion, adaptive combination weighting, transformer