图像分析在组织病理学和细胞病理学:从早期到目前的观点
Tibor Mezei1, Melinda Kolcsár2, András Joó3
1Department of Pathology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania.
Journal of imaging
|October 25, 2024
概括
病理学和细胞病理学中的图像分析正在从手动解释演变为先进的计算机辅助技术. 机器学习和人工智能正在通过数字病理学提高诊断准确性和患者的治疗结果.
科学领域:
- 病理学和细胞病理学
- 医学成像分析 医学成像分析
- 计算生物学 计算生物学
背景情况:
- 病理学和细胞病理学传统上依赖于专家病理学家的手动显微镜分析.
- 早期的图像分析由于计算能力和样本复杂性而面临限制.
- 数字成像和计算工具正在改变传统的诊断方法.
研究的目的:
- 审查图像分析在病理学和细胞病理学中的演变和影响.
- 突出数字病理学,机器学习和人工智能的作用.
- 讨论提高诊断准确度和个性化医学的未来前景.
主要方法:
- 对病理学中的历史和当前图像分析技术的审查.
- 数字病理学,机器学习和人工智能的整合.
- 分析计算病理学和诊断决策支持方面的进展.
主要成果:
- 计算机辅助分析可以更客观,更有效地解释病理图像.
- 机器学习和人工智能集成正在改善诊断能力.
- 数字病理学数据为提高准确性和预后预测提供了动力.
结论:
- 病理学和细胞病理学的未来越来越依赖于先进的计算机辅助图像分析.
- 提高诊断准确度和个性化治疗策略是预期的结果.
- 通过该领域的技术进步,预计会改善患者的治疗结果.
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