ABVS breast tumour segmentation via integrating CNN with dilated sampling self-attention and feature interaction Transformer.

Given the rapid increase in breast cancer incidence, the Automated Breast Volume Scanner (ABVS) is developed to screen breast tumours efficiently and accurately. However, reviewing ABVS images is a challenging task owing to the significant variations in sizes and shapes of breast tumours. We propose a novel 3D segmentation network (i.e., DST-C) that combines a convolutional neural network (CNN) with a dilated sampling self-attention Transformer (DST). In our network, the global features extracted from the DST branch are guided by the detailed local information provided by the CNN branch, which adapts to the diversity of tumour size and morphology. For medical images, especially ABVS images, the scarcity of annotation leads to difficulty in model training. Therefore, a self-supervised learning method based on a dual-path approach for mask image modelling is introduced to generate valuable representations of images. In addition, a unique postprocessing method is proposed to reduce the false-positive rate and improve the sensitivity simultaneously. The experimental results demonstrate that our model has achieved promising 3D segmentation and detection performance using our in-house dataset. Our code is available at: https://github.com/magnetliu/dstc-net.