开源工具检测病理图像模型中的偏差与过拟合问题
Open-source framework for detecting bias and overfitting for large pathology images
- 提出通用无模型依赖的调试框架，适配大规模病理图像
- 可在消费级显卡工作站运行，支持发现背景色等非相关特征
- 适合医学影像、深度学习可靠性研究者使用
即使在包含数十亿样本的数据集上训练的基础模型也可能产生导致过拟合和偏差的捷径，例如背景颜色或色彩强度等非相关模式。为确保深度学习应用的鲁棒性，亟需检测并消除这些捷径。当前模型调试方法耗时且需针对特定模型架构定制。本文提出一种通用、模型无关的调试框架，聚焦于需要大模型与大量计算资源的组织病理学领域。该框架可在配备普通GPU的工作站上运行。我们验证了其能复现自监督学习模型中已知的非图像捷径，并识别出基础模型中的潜在捷径。简便易用的测试有助于提升全视野切片图像（WSI）分析模型的可靠性、准确性和泛化能力。框架已开源，可于GitHub获取。
原文摘要 · Abstract (English)
Even foundational models that are trained on datasets with billions of data samples may develop shortcuts that lead to overfitting and bias. Shortcuts are non-relevant patterns in data, such as the background color or color intensity. So, to ensure the robustness of deep learning applications, there is a need for methods to detect and remove such shortcuts. Today's model debugging methods are time consuming since they often require customization to fit for a given model architecture in a specific domain. We propose a generalized, model-agnostic framework to debug deep learning models. We focus on the domain of histopathology, which has very large images that require large models - and therefore large computation resources. It can be run on a workstation with a commodity GPU. We demonstrate that our framework can replicate non-image shortcuts that have been found in previous work for self-supervised learning models, and we also identify possible shortcuts in a foundation model. Our easy to use tests contribute to the development of more reliable, accurate, and generalizable models for WSI analysis. Our framework is available as an open-source tool available on github.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。