深度学习在膀胱癌病理学中的研究进展

Research Progress of Deep Learning in Bladder Cancer Pathology

膀胱癌的发病率逐年上升,其诊断的金标准依赖于组织病理活检。全载玻片数字化技术可产生大量高分辨率捕获的病理图像,促进了数字病理学的发展。随着人工智能的热潮掀起,深度学习作为人工智能的一种新方法,已经在膀胱癌的肿瘤诊断、分子分型、预测预后和复发等病理图像分析中取得了显著成果。传统病理极度依赖于病理学家的专业水平和经验储备,主观性强且可重复性差。深度学习以其自动提取图像特征的能力,在辅助病理学家进行决策时,可提高诊断效率和可重复性,降低漏诊和误诊率。这不仅能缓解目前面临人才短缺和医疗资源不均的压力,而且也能促进精准医疗的发展。本文就深度学习在膀胱癌病理图像分析中的最新研究进展和前景作一述评。

The incidence of bladder cancer is increasing annually,and the gold standard for its diagnosis relies on histopathological biopsy.Whole-slide digitization technology can produce thousands of high-resolution captured pathological images and has greatly promoted the development of digital pathology.Deep learning,as a new method of artificial intelligence,has achieved remarkable results in the analysis of pathological images for tumor diagnosis,molecular typing,and prediction of prognosis and recurrence of bladder cancer.Traditional pathology relies heavily on the professional level and experience of pathologists;as such,it is highly subjective and has poor reproducibility.Deep learning can automatically extract image features.It can also improve diagnostic efficiency and repeatability and reduce missed and misdiagnosed rates when used to assist pathologists in making decisions.This technology cannot only alleviate the pressure of the current shortage of skilled workforce and uneven medical resources but also promote the development of precision medicine.This article reviews the latest research progress and prospects of deep learning in pathological image analysis of bladder cancer.