计算病理学:一项调查审查和前进的道路
Mahdi S Hosseini1, Babak Ehteshami Bejnordi2, Vincent Quoc-Huy Trinh3
1Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada.
Journal of pathology informatics
|February 29, 2024
概括
计算病理学 (CPath) 使用人工智能分析医疗图像进行癌症诊断. 这份对800多篇论文的综述强调了将CPath工具整合到临床实践中的挑战和未来方向.
科学领域:
- 计算病理学 (CPath) 作为一个跨学科领域.
- 整合计算方法用于医疗组织病理学图像分析.
背景情况:
- 数字病理学使得癌症诊断中的深度学习和计算机视觉数据流成为可能.
- 尽管进行了广泛的研究,但在采用CPath算法进入临床实践方面存在重大差距.
研究的目的:
- 为超过800篇计算机病理学论文提供全面的评论.
- 解决从问题设计到应用和CPath工具的实施所面临的挑战.
- 绘制CPath的当前景观和未来方向的地图.
主要方法:
- 通过检查关键工作和挑战,将论文编入模型卡.
- 在以数据为中心,以模型为中心和以应用为中心的视角上分析CPath的发展.
- 审查CPath开发和集成的全周期中的挑战.
主要成果:
- 确定CPath开发和临床整合中的关键挑战.
- 对当前计算病理学研究现状的结构化概述.
- 800多篇论文的目录,以及相关的模型卡片,供社区参考.
结论:
- 突出了CPath技术开发和临床整合的剩余挑战.
- 为计算病理学的未来进步提供方向.
- 促进社区对癌症诊断和治疗的CPath工具的理解和采用.
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