# MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
## 5 Experimental Results and Discussion
Figure 5: Qualitative comparisons (**a**) Histopathology image (**b**) Ground truth (**c**) Nested UNet (**d**) Attention UNet (**e**) RCNN UNet (**f**) MIU-net. is still huge. The score of our model is DSC 0.821, HD 51.0, which is higher than DSC of traditional Attention U-net. The inner and outer edges of the nucleus achieve better segmentation result. Table 3 shows ablation experiments results. We conducted ablation experiments on the model. The baseline model is unet. The addition of two modules shows a practical improvement in segmentation accuracy. After adding the attention module separately, it shows that the attention mechanism expression is a little better on top of HD, with 1.2 more. This indicates that the attention mechanism is applied to the edge of the nucleus for better learning. While in the final accuracy score, the two are similar and mechanisms plays the same role in improving performance. The highest score was achieved in the final model that combines the two modules, and our model proved effective. Class activation mapping is a visual thermodynamic chart that has certain explanatory functions for net. It overlays highly abstract category feature maps for visualization to obtain heat maps. One of the most essential techniques is global average pooling, which locates feature pixels. We conducted class activation mapping that only does activation mapping of the nuclei. The heat map is better to observe which patches are important, and give different weights to the deep feature map, to sum up to obtain saliency map. Figure 6 show that activation was performed in two nucleus-dense regions, while the other models did not identify the nucleus-dense regions. In comparison, activation was performed in two relatively non-dense nucleus regions. The second example has the correct activation in the nuclear edge region of the ring, giving more weight to the ring region, and a small area outside the ring is also activated. While other models only activate the outer region of the ring without identifying the important nucleus of the ring. This indicates that our network is better focused on cancerous nuclei, which gradually reduces reliance of manual annotation and timely monitoring effectiveness of net learning. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **DSC** & **HD** & **AUC** \\ \hline Baseline & 0.757 & 47.8 & 0.78 \\ \hline U-net + MIS & 0.801 & 49.1 & 0.89 \\ \hline U-net + AS & 0.812 & 50.3 & 0.88 \\ \hline MIU-net & 0.821 & 51.0 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation Experiments Results. Figure 6: Class activation mapping (**a,d**) Histopathology image (**b,e**) U-net (**c,f**) MIU-net. Figure 7 illustrate the overlaid segmented mask of one of the test images in the kumar dataset for some models. Some nets unable to detect all ground masked nuclei in the picture, was unable to detect shape variability of nuclei, and missed some touching nuclei. Our architectures used made many accurate detections. The boundary distribution of touching nuclei is very close to each other, which can easily result in the ability to split two or more contact nuclei. These results indicated that the main challenge of semantic segmentation of the nucleus in irregular shape of the nucleus and separation of multiple nuclei with overlap. Our models has a good advantage compared to the base model in addressing these challenges. In Figure 8, loss converges to stability at about 75 epochs, and the numerical value is stable below 0.1. Bee and dice loss are combined in loss to carry out weighted calculation for both small targets and large areas. Loss fluctuates in the early stage. The definition of _Acc_ indicator is to divide the number of correctly predicted pixels by the total number of pixels in the picture. \[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
Figure 8: Visual results (**a**) loss (**b**) Acc. Figure 7: Overlaid segmented mask (**a**,**d**) Ground truth (**b**,**e**) U-net (**c**,**f**) MIU-net.