Spider-Net: High-resolution multi-scale attention network with full-attention decoder for tumor segmentation in kidney, liver and pancreas.

The abdominal tumor is a general term for tumors in kidney, liver and pancreas. Accurate segmentation of abdominal tumors is essential for their treatment. However, the varying shapes and sizes of abdominal organs result in significant differences in tumor regions. Existing convolution neural networks (CNNs) can only accurately segment individual abdominal tumors, lacking sufficient generalizability. We aim to design a network that can achieve good segmentation results for different abdominal tumors. To this end, a Spider-net to segment tumors is presented in this paper, which consists of a high-resolution multi-scale attention encoder and a full-attention decoder. Additionally, scale attention that integrates channel attention and spatial attention is designed for generating output. We have also designed a classification branch to distinguish whether the segmented region is a real tumor area or another benign lesion. We train and evaluate the Spider-net on three different organs: the kidney, pancreas, and liver. Spider-net achieves state-of-the-art results compared to methods that only use CNNs or transformers. Code are available at https://github.com/h2440222798/HRMA. [Display omitted] • Convolution and vision transformer are combined in a high-resolution structure. • A lightweight patch embedding and cross-layer fusion are employed for reducing the parameters of the encoder. • The fusions of different attention modules are proposed for decoding. • Incorporating both channel and spatial attention into the scale attention for generating the output. • A new classification branch are proposed for classify if current slice contains tumor. [ABSTRACT FROM AUTHOR]