计算机病理学中的机器学习:挑战和机遇
Michael Cooper1,2,3, Zongliang Ji1,3, Rahul G Krishnan1,3,4
1Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Genes, chromosomes & cancer
|June 14, 2023
概括
机器学习和深度学习正在使用数字组织病理学图像彻底改变癌症诊断. 这些计算工具自动化风险预测和分层,改善瘤学工作流程.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在瘤学中
背景情况:
- 组织病理学图像对于癌症诊断和分期至关重要.
- 数字化已经将分析从显微镜转移到计算机.
- 机器学习 (ML) 和深度学习 (DL) 已经成为强大的分析工具.
研究的目的:
- 审查计算机模型在组织病理学中的兴起.
- 突出 ML/DL 自动化的临床任务.
- 讨论 ML 技术和该领域的未来机会.
主要方法:
- 机器学习和深度学习应用在计算病理学中的审查.
- 分析自动预测和风险分层模型.
- 讨论各种ML技术应用于数字化组织病理学幻灯片.
主要成果:
- 在大型数据集上训练的ML模型在自动预测和风险分层方面取得了成功.
- 在瘤学工作流程中的临床任务自动化方面取得了重大进展.
- 计算工具的出现,用于分析数字化组织病理学图像.
结论:
- 机器学习和深度学习正在通过计算基因病理学改变癌症诊断.
- 这些技术为改善患者风险分层和自动化临床决策提供了机会.
- 对机器学习技术的进一步研究可以解决未解决的问题,并增强未来的应用.
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