开源框架用于检测偏差和超拟合的大型病理图像
Anders Sildnes1, Nikita Shvetsov1, Masoud Tafavvoghi2
1Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
PloS one
|February 19, 2026
概括
一个新的框架检测和删除深度学习模型中不相关的文物,提高整个幻灯片图像分析的可靠性. 这种模型不可知的方法增强了概括性,并减少了人工智能应用中的偏见.
科学领域:
- 人工智能的人工智能
- 计算病理学计算病理学
- 医疗成像医学成像
背景情况:
- 基础模型可以从不相关的图像文物 (如背景颜色) 中学习偏见.
- 现有的检测和删除这些文物的方法通常是特定于域名,特定于模型,并且计算成本昂贵.
- 这限制了医疗图像分析中强大的深度学习的广泛采用.
研究的目的:
- 开发一个模型-架构-不可知框架,用于调试深度学习模型.
- 解决需要可靠的方法来检测和删除不相关的文物.
- 提高用于全幻灯片图像 (WSI) 分析的模型的稳定性和通用性.
主要方法:
- 开发了一个新的,模型-架构-不可知调试框架.
- 在一个带有非常大的图像的大型组织病理学数据集上测试了框架.
- 使用预训练 (Phikon-v2) 和自我监督 (MoCo v1) 模型进行评估.
主要成果:
- 该框架成功地在两个测试模型中复制了已知的偏差模式.
- 证明了对背景颜色等非相关文物依赖的检测能力.
- 展示了框架在WSI分析中识别模型偏差的实用性.
结论:
- 拟议的框架有助于为WSI分析开发更可靠,更准确的深度学习模型.
- 模型不可知性方法提高了调试方法的通用性.
- 该开源工具与MONAI框架集成,促进了更广泛的使用和开发.
相关概念视频
01:18Imaging Biological Samples with Optical Microscopy
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
01:37Super-resolution Fluorescence Microscopy
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.