Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation

Abstract

Malignant tumors are a common cytopathologic disease. Pathological tissue examination is a key tool for diagnosing malignant tumors. Doctors need to manually analyze the images of pathological tissue sections, which is not only time-consuming but also highly subjective, easily leading to misdiagnosis. Most of the existing computer-aided diagnostic techniques focus too much on accuracy when processing pathological tissue images, and do not take into account the problems of insufficient resources in developing countries to meet the training of large models and the difficulty of obtaining medical annotation data. Based on this, this study proposes an artificial intelligence multiprocessing scheme (MSPInet) for digital pathology images of malignant tumors. We use techniques such as data expansion and noise reduction to enhance the dataset. Then we design a coarse segmentation method for cell nuclei of pathology images based on Transformer for Semantic Segmentation and further optimize the segmentation of tumor edges using conditional random fields. Finally, we improve the training strategy for knowledge distillation. As a medical assistive system, the method can quantify and convert complex pathology images into analyzable image information. Experimental results show that our method performs well in terms of segmentation accuracy and also has advantages in terms of time and space efficiency. This makes our technology available to developing countries that are not as well resourced, and equipped in terms of medical care. The teacher model and lightweight student model included in our method achieve 71.6% and 66.1% Intersection over Union (IoU) in cell segmentation respectively, outperforming Swin-unet and CSWin Transformer.