EvoPS:在整个幻灯片图像的培训嵌入空间中的进化补丁选择
Saya Hashemian1, Azam Asilian Bidgoli1
1Department of Computer Science and Physics, Wilfrid Laurier University, ON, Canada.
Artificial intelligence in medicine
|March 5, 2026
概括
计算病理学面临着大型全幻灯片图像 (WSIs) 的挑战. 进化补丁选择 (EvoPS) 将补丁数量减少90%以上,同时保持诊断准确度,以便更好地进行WSI分析.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在医疗保健中的应用
背景情况:
- 计算病理学中的全幻灯片图像 (WSIs) 是千兆像素大小的,需要分解成众多的补丁.
- 高维的补丁嵌入在计算上是昂贵的,并且经常包含不信息的区域.
- 目前的补丁选择方法缺乏对表示紧性和诊断准确性的明确优化.
研究的目的:
- 开发一种新的框架,即进化补丁选择 (EvoPS),用于在WSIs中高效和准确的补丁选择.
- 为了解决选择的补丁数量和下游任务执行之间的权衡问题.
- 创建一个最佳解决方案的帕雷托前线,平衡计算成本和诊断准确性.
主要方法:
- 在培训嵌入空间内,将补丁选择作为一个多目标优化问题的制定.
- 采用进化搜索,同时最大限度地减少选定的补丁嵌入,最大限度地提高相似性搜索性能.
- 从癌症基因组图谱 (TCGA) 采用五种组织病理学基础模型,验证了EvoPS框架在四个主要癌症队列中的有效性.
主要成果:
- EvoPS显著减少了所需的训练补丁数量超过90%.
- 与最先进的方法相比,该框架始终保持或改进F1分数的分类.
- EvoPS产生了更高质量的训练表示,减少了内存需求,并改善了信号噪声比.
结论:
- EvoPS提供了一种强大且有原则的方法,用于创建高效,准确和可解释的WSI表示.
- 该框架使用户能够优化计算成本和诊断性能之间的平衡.
- 通过提高WSI分析效率和准确性,EvoPS提高了计算病理学的实用性.
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