Nuclear Segmentation in Histological Images Using Multiple Attention System Mixing

Cancer remains a major global health threat due to its high mortality rate and the challenges associated with its treatment, especially in the later stages. It is very important that the patient identify the cancer as soon as possible to increase recovery chances, and for this, histological images are often used. These images are often looked at by a professional, who analyzes it and categorizes the tissue in labels, but it is often a difficult process for these professionals to analyze a great number of images, and that is why it is used computer vision and neural networks to aid in the identification steps of the disease. A very important network structure that can be used in computer vision is a model called U-Net, named after the U shape made by the decoding and encoding blocks, this network model extracts information while changing the size of the image, being able to get the finest to the more general features of the image. This network can also use an attention system to further improve the feature extraction phase and aid in even better segmentation, with multiple attention models for different purposes. Therefore, this study shows how these attention channels can be tweaked to improve the model results, allowing different types of attention to improve in areas of weakness of the model.