School of Computer Science and Technology, Jiangxi University of Chinese Medicine, Nanchang, 330004, Jiangxi, China.
Research Center for Artificial Intelligence, Monash University, Clayton, Melbourne, VIC, 3800, Australia.
Sci Rep. 2024 Oct 22;14(1):24834. doi: 10.1038/s41598-024-76577-6.
Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model's novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise.
疾病的诊断严重依赖于细胞病理学图像，但在相关细胞的手动识别中，每个图像都需要大量的人工，尤其是在合格的医疗保健专业人员匮乏的地区。本研究旨在开发一个智能系统，通过解决图像噪声和分割问题来提高细胞病理学图像的诊断准确性，从而提高医疗专业人员的疾病诊断效率。我们引入了一个创新的系统，该系统结合了一种自监督算法（SDN），用于图像去噪，并使用 UPerMVit 模型进行数据增强和图像分割。与传统方法相比，UPerMVit 模型新颖的注意力机制和模块化架构提供了更高的准确性和更低的计算复杂度。所提出的系统有效地降低了图像噪声，并准确地分割了标注图像，突出了与医务人员相关的细胞结构。这提高了诊断的准确性，并有助于准确识别病理细胞。我们的智能系统为医疗专业人员提供了一种可靠的工具，提高了细胞病理学图像分析的诊断效率和准确性。它为缺乏足够医疗专业知识的地区提供了重要的技术支持。
