It is more practical and robust for some complex tasks in biomedical areas, such as saliency detection and image segmentation. Therefore, it is crucial to work out an effective unsupervised salient segmentation method for biomedical images. However, in recent years, few works have looked into unsupervised salient segmentation on biomedical images due to the complexity of biological structures. The authors in [11] systematically reviewed current unsupervised models for biomedical image segmentation. More recently, a unified unsupervised approach based on clustering and deep representation learning was designed by [12]. The authors in [13] proposed a teacher-student unsupervised learning system. The teacher performs unsupervised object discovery and, at the same time, multiple students with various network architectures are trained to ensure a better diversity. The authors [14] utilized the the squeeze and excitation network to capture interdependencies between channels and iteratively enable the CNN to learn the target generated by K-Means clustering. These methods are either based on super-pixel clustering with posterior selection or dependent on carefully optimized network hyper-parameters, which indicates that they are in need of human interference and are not completely unsupervised. In this paper, we propose the PUB-SalNet, which is a self-aware network based on pre-training and attentional backpropagation. In order to build a high-performance automatic biomedical salient segmentation model to improve computer-aided diagnosis and other biomedical image analyzing tasks, we design a processing pipeline with three major modules: (1) A pre-training method specially designed for biomedical images; (2) The U-SalNet model, which selectively attends to salient objects via fusing two attention mechanisms into U-Net; (3) An unsupervised self-aware backpropagation method based on superpixels, which iteratively updates the parameters of U-SalNet. Quantitative and  
Figure 1: An unsupervised example of semantic segmentation and salient segmentation on CECT images. qualitative experiments comparing our performance with state-of-the-art salient segmentation methods demonstrate that the proposed PUB-SalNet outperforms all existing unsupervised methods and achieves state-of-the-art performance on the simulated biomedical data sets. Furthermore, PUB-SalNet can also be easily extended to 3D images. The experimental results on 2D and 3D biomedical data sets show generalization ability and robustness of the proposed method. Our main contributions are summarized as follows:  
* We propose the novel PUB-SalNet model for biomedical salient segmentation, which is a completely unsupervised method utilizing weights and knowledge from pre-training and attention-guided refinement during back propagation. * We aggregate a new biomedical data set called SalSeg-CECT, featuring rich salient objects, different SNR settings, and various resolutions, which also serves for pre-training and fine-tuning for other complex biomedical tasks. * Extensive experiments show that the proposed PUB-SalNet achieves state-of-the-art performance. The same method can be adapted to process 3D images, demonstrating correctness and generalization ability of our method. The rest of the paper is organized as follows. We review related works in the next section. In Section 3, we describe the PUB-SalNet and the completed processing pipeline for salient segmentation. Quantitative and qualitative experiments are discussed in detail in Section 4. Lastly, in Section 5, we conclude our method and future works.