Comparison of cell image segmentation results based on UNet and transformer

With the rapid development of artificial intelligence and big data technologies, the healthcare industry is undergoing a digital transformation. This research aims to apply artificial intelligence technology to improve the efficiency of healthcare delivery and patient experience. Malignant tumor seriously endangers people's life and health, so early screening, diagnosis and treatment are very important. The diagnosis of cancer depends on pathological diagnosis. Tissue biopsy and imaging observation of tumor cells are important means of clinical diagnosis. In recent years, great progress has been made in machine learning methods applied to histopathological image analysis, especially deep learning-based methods to improve diagnostic efficiency and accuracy. The image segmentation methods adopted in this study include traditional methods and deep learning methods, using models such as UNet and Transformer. By obtaining data set from Kaggle, image preprocessing, data enhancement and other operations are carried out, and TransUNet and UNet models are used to segment and make statistics on the data set. The results show that TransUNet can detect more cell numbers in most cases, while UNet may be more accurate in estimating cell size and coverage. The UNET-based segmentation technique can also achieve good results on small data sets, and can be used as a basic step for further analysis of cancer cells.