AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights

Authors

  • Xiaonan Xu Independent Researcher, Northern Arizona University, Flagstaff, USA
  • Hongjie Niu Independent Researcher, Sichuan Conservatory University of Music, Chengdu, China
  • Huan Ji Information Science, Trine University, phoenix, AZ, USA
  • Hao Li Independent Researcher, Sichuan Conservatory University of Media and Communications, Chengdu, China
  • Jiufan Wang Independent Researcher, William & Mary, Williamsburge, VA, USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(03).05

Keywords:

Tumor tissue, Pathological image, Modern medicine, Intelligent diagnosis

Abstract

The traditional diagnosis model of tumor pathology depends on the experience of the doctor, which is inevitably subjective. With the rapid development of modern medical imaging, medical imaging technology has formed a medical imaging system composed of UI, CT, CR, DR, MRI, PET, PET-CT, digital subtraction angiography and PACS by traditional single ordinary X-ray and angiography. The continuous enrichment of imaging technology has changed medical imaging from "auxiliary examination means" to the most important clinical diagnosis and differential diagnosis method in modern medicine. With the application of digital scan sections in clinicopathology, the computer technology of artificial intelligence (AI) assisted diagnosis has developed rapidly in the analysis of tumor tissue images. This paper summarizes the application progress of AI in tumor pathology, and describes the exploration and application of AI in the field of quantitative analysis of histopathological molecular markers closely related to clinical diagnosis and treatment in recent years, so as to provide useful reference for the development of tumor intelligent diagnosis and treatment model.

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Published

2024-03-19

How to Cite

Xu, X., Niu, H., Ji, H., Li, H., & Wang, J. (2024). AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights. Journal of Theory and Practice of Engineering Science, 4(03), 21–27. https://doi.org/10.53469/jtpes.2024.04(03).05