# MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
###### Abstract  
In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods.  
n pacs: nuclei segmentation; histopathology image; attention mechanism; efficient network +
Footnote †: journal: Medical  
which tends to extract too many bloated semantic features in the shallow layer and not enough in the deep layer. We would like to make appropriate optimizations in the structure and depth of the network. In this work, we focus on adopting different methods in different depth convolutional layers. Nuclear segmentation is a relatively simple binary classification task. The network channels of MIU-net is reduced to half of the routine U-net network [2]. Shallow layers use single convolution modules, and deep layers combine inception and resnet ideas [3]. We adopt residual connections and different sizes kernels to extract features in deep layers. The function of the attention module is to train for the region of interest in the decoder process [4]. In this paper, we attempt to use the U-net as our baseline and make some modifications to it. This module can achieve state-of-the-art performance compared to the original U-net and other variants on kumar dataset. We have improved performance by adding the attention mechanism to the original U-net, improving the inception module, and appropriately reducing the number of channels. We have obtained the appropriate range of parameter quantity, taking into account both parameter quantity and accuracy.
* The pre-processing part combined the advantages of various data enhancement to make the histopathology images clearer and higher contrast.
* A new network architecture is proposed, which has a certain robustness and efficiency while reducing parameters and maintaining good segmentation performance.
* The modules of the proposed architecture address challenges, focusing on a robust modified inception block and attention block that segment touching nuclei and accurately detecting nuclei with variable shapes.