Cytopathology image analysis method based on high-resolution medical representation learning in medical decision-making system

• Published in: Complex & intelligent systems Vol. 10; no. 3; pp. 4253 - 4274
• Main Authors: Li, Baotian, Liu, Feng, Lv, Baolong, Zhang, Yongjun, Gou, Fangfang, Wu, Jia
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2024; Springer Nature B.V; Springer
• Subjects: Artificial intelligence; Assisted analysis; Complexity; Computational Intelligence; Conditional random fields; Cytopathology images; Data Structures and Information Theory; Distillation; Engineering; High resolution; Image analysis; Image enhancement; Image resolution; Image segmentation; Knowledge sublimation; Machine learning; Medical imaging; Nuclei (cytology); Original Article; Pathology; Representations; Technology assessment; Assisted analysis; Image segmentation; Knowledge sublimation; Cytopathology images; Artificial intelligence
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-024-01390-7

Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.