无需共享数据即可实现医疗影像的分布外检测，保障模型安全部署。
DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging
- 通过二分类任务量化测试样本与训练数据的分离难度，实现分布外检测。
- 仅交换模型参数即可完成远程数据对比，在保护隐私前提下提升检测效果。
- 适用于医疗影像领域，适合需高安全性部署的AI模型开发者使用。
在医疗影像等关键应用中，机器学习模型的安全部署需要识别训练时未见的输入（即分布外，OOD），以避免不可靠预测。现有方法通常在部署后丢弃训练数据，或假设测试与训练数据集中存储，但现实中受限于数据规模及隐私保护，难以实现数据共享。本文提出隔离网络（Isolation Network），通过求解二分类任务量化测试样本与训练数据的分离难度。进一步提出去中心化隔离网络（DIsoN），在无法共享数据的情况下，仅通过交换模型参数即可实现训练与测试数据的远程对比。引入类别条件机制，仅与同类别训练数据比较。在皮肤科、胸部X光、乳腺超声和组织病理学四个医学影像数据集上，12个分布外检测任务中，DIsoN表现优于现有方法，且符合数据隐私要求。该框架为模型开发者提供了可附加的远程安全分布外检测服务。代码已开源。
原文摘要 · Abstract (English)
Safe deployment of machine learning (ML) models in safety-critical domains such as medical imaging requires detecting inputs with characteristics not seen during training, known as out-of-distribution (OOD) detection, to prevent unreliable predictions. Effective OOD detection after deployment could benefit from access to the training data, enabling direct comparison between test samples and the training data distribution to identify differences. State-of-the-art OOD detection methods, however, either discard the training data after deployment or assume that test samples and training data are centrally stored together, an assumption that rarely holds in real-world settings. This is because shipping the training data with the deployed model is usually impossible due to the size of training databases, as well as proprietary or privacy constraints. We introduce the Isolation Network, an OOD detection framework that quantifies the difficulty of separating a target test sample from the training data by solving a binary classification task. We then propose Decentralized Isolation Networks (DIsoN), which enables the comparison of training and test data when data-sharing is impossible, by exchanging only model parameters between the remote computational nodes of training and deployment. We further extend DIsoN with class-conditioning, comparing a target sample solely with training data of its predicted class. We evaluate DIsoN on four medical imaging datasets (dermatology, chest X-ray, breast ultrasound, histopathology) across 12 OOD detection tasks. DIsoN performs favorably against existing methods while respecting data-privacy. This decentralized OOD detection framework opens the way for a new type of service that ML developers could provide along with their models: providing remote, secure utilization of their training data for OOD detection services. Code: https://github.com/FelixWag/DIsoN
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