Self-supervised few-shot medical image segmentation with multi-attention mechanism
Mainstream fully supervised deep learning segmentation models can achieve good results when trained on abundant labeled data,but the image segmentation in the medical field faces the chal-lenges of high annotation cost and diverse segmentation targets,often lacking sufficient labeled data.The model proposed in this paper incorporates the idea of extracting labels from data through self-super-vision,utilizing superpixels to represent image characteristics for image segmentation under conditions of small sample annotation.The introduction of multiple attention mechanisms allows the model to fo-cus more on spatial features of the image.The position attention module and channel attention module aim to fuse multi-scale features within a single image,while the external attention module highlights the connections between different samples.Experiments were conducted on the CHAOS healthy abdominal organ dataset.In the extreme case of the 1-shot,DSC reached 0.76,which is about 3%higher than the baseline result.In addition,this paper explores the significance of few-shot learning by adjusting the number of N-way-K-shot tasks.Under the 7-shot setting,DSC achieves significant improvement,which is within an acceptable range of the segmentation effect based on full supervision based on deep learning.
万方数据
维普
