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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.<\/jats:p>","DOI":"10.1007\/s40747-024-01471-7","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T09:02:12Z","timestamp":1716454932000},"page":"5831-5849","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0453-8222","authenticated-orcid":false,"given":"Fangfang","family":"Gou","sequence":"first","affiliation":[]},{"given":"Xinrong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"issue":"3","key":"1471_CR1","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1109\/TMI.2022.3217218","volume":"42","author":"Y Han","year":"2023","unstructured":"Han Y, Holste G, Ding Y, Tewfik A, Peng Y, Wang Z (2023) Radiomics-guided global-local transformer for weakly supervised pathology localization in chest X-Rays. 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