# MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
## 2 Related Work  
A large number of scientific studies have been conducted on semantic segmentation of pixel-level pathology pictures by conventional methods [5; 6; 7; 8]. The adaptive threshold method is an innovation on the method of setting the threshold manually. The threshold setting is simple and easy to implement, but the segmentation effect is terrible and sensitive to image noise [9]. Watershed segmentation is prone to the phenomenon of excessive segmentation [10]. Automatic Bayesian algorithm achieves high accuracy [11]. There is fuzzy clustering [12], graph-based segmentation methods [13], and so on.  
Automatic analysis of medical images based on deep convolutional neural networks has attracted much interest owing to its excellent performance. We will summarize the most recent advances in nuclear segmentation of histopathological images. U-net is a landmark work where the basic framework is a encoder-decoder convolutional neural network with skip connections. It has a symmetric network structure with feature extraction in the front part and upsampling in the second part, using skip connections to fuse the location information at the bottom with the deep semantic information [14]. The advantage of it is simple and efficient and has become the backbone of almost all primary medical image segmentation methods. There are many structural variants of U-net. Nested U-net can fuse networks of different depths and produce flexible feature fusion schemes [15]. Attention mechanisms are effective for nuclear segmentation. The hard attention mechanism directly removes useless target and only trains the most important foreground nuclei. The soft attention mechanism can process channels, spatial and mixed domain. Channel attention mechanism suppresses irrelevant channels, models the importance of each feature, and for different tasks can be assigned features based on the input. [16]. Spatial attention mechanism first locates the pixel points relevant to the task and then proceeds an affine transformation or obtains the weights. [17]. The U-net with attention module allows more accurate features extraction for the foreground cell nucleus fraction [18]. The one with rcnn module is generalized from the target detection domain and extracts features with a sliding window of selective search [19]. U2-net uses U-net structures inside multiple encoding and decoding processes. Then combines the results of more U-net outputs. Finally merger results [20]. The context encoding network apply dense dilated convolution to extract features using different scales of the field of view, and multiple pooling modules reduce the complexity of the model [21].  
New network architectures or multi-branching tasks applied to different kinds of medical images. HoVer-net can process classification tasks according to cell nucleus ground classification labels and better handles joining-up nucleus pixels. It puts forward horizontal distance gradient technique to settle problems [22]. A novel deep contour-aware network portray clear contour lines to separate cell nuclei instance, which intelligently integrates contextual information with auxiliary supervision. Then it constructed a depth contour-aware network by fusing complementary information with appearance and contour of the cell nucleus [23]. Neural network architecture uses graph structure to analyze bone age presented in X-rays [24]. A characteristic pyramidal network was used in the retinal classification of lesions caused by diabetes and grading of lesion levels [25]. Multimodal and single-modal feature reconstruction in brain CT scans for efficient multiple branching tasks [26]. MIU-net can use a variety of modules to improve segmentation efficiency. Furthermore, the use of publicly available datasets, which are annotated by professional physicians, further enhances the challenge results.