WISE:有效的WSI选择,用于在组织病理学中积极学习
Hyeongu Kang1, Mujin Kim1, Young Sin Ko2
1Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
概括
积极学习通过选择信息全幻灯片图像 (WSIs) 来增强用于医学成像的深度神经网络. 这种新方法,WISE,显著减少了改善诊断模型性能所需的WSIs的数量.
科学领域:
- 医学图像分析 医学图像分析
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 深度神经网络 (DNN) 在医学图像分析中实现了高性能,但需要不断改进.
- 积极学习 (AL) 是一个有前途的策略,用于增强DNN在医疗领域.
- 目前在组织病理学中的AL方法专注于图像补丁,忽视了全幻灯片图像 (WSI) 选择,这限制了性能增长.
研究的目的:
- 引入一种新的WSI级活跃学习方法,即WSI信息选择 (WISE).
- 通过专注于信息化的WSI选择来解决基于补丁的AL的局限性.
- 为了提高DNN模型在组织病理学中的性能和效率,使用WSI级AL.
主要方法:
- 开发了WISE,这是一个WSI级的AL方法,使用了一个新的类距离度量.
- 智能识别了不同的和不确定的WSIs用于模型培训.
- 在现实世界的结肠和胃数据集以及公共的DigestPath数据集上评估了WISE.
主要成果:
- 在所有测试的数据集中,WISE实现了最先进的性能.
- 与传统的单池设置相比,该方法显著减少了所需的WSI的数量,超过三倍.
- 证明了WSI级AL在提高DNN性能方面的有效性.
结论:
- WISE提供了一种有效和可持续的策略,用于增强在组织病理学中的DNNs.
- WSI级的AL对于优化癌症诊断中的深度学习模型开发至关重要.
- WISE提高了模型性能,同时大大降低了数据需求.
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