MSAByNet: A multiscale subtraction attention network framework based on Bayesian loss for medical image segmentation

Medical image segmentation is a critical and complex process in medical image processing and analysis. With the development of artificial intelligence, the application of deep learning in medical image segmentation is becoming increasingly widespread. Existing techniques are mostly based on the U-shaped convolutional neural network and its variants, such as the U-Net framework, which uses skip connections or element-wise addition to fuse features from different levels in the decoder. However, these operations often weaken the compatibility between features at different levels, leading to a significant amount of redundant information and imprecise lesion segmentation. The construction of the loss function is a key factor in neural network design, but traditional loss functions lack high domain generalization and the interpretability of domain-invariant features needs improvement. To address these issues, we propose a Bayesian loss-based Multi-Scale Subtraction Attention Network (MSAByNet). Specifically, we propose an inter-layer and intra-layer multi-scale subtraction attention module, and different sizes of receptive fields were set for different levels of modules to avoid loss of feature map resolution and edge detail features. Additionally, we design a multi-scale deep spatial attention mechanism to learn spatial dimension information and enrich multi-scale differential information. Furthermore, we introduce Bayesian loss, re-modeling the image in spatial terms, enabling our MSAByNet to capture stable shapes, improving domain generalization performance. We have evaluated our proposed network on two publicly available datasets: the BUSI dataset and the Kvasir-SEG dataset. Experimental results demonstrate that the proposed MSAByNet outperforms several state-of-the-art segmentation methods. The codes are available at https://github.com/zlxokok/MSAByNet.