Bercea Cosmin I, Wiestler Benedikt, Rueckert Daniel, Schnabel Julia A
Chair of Computational Imaging and AI in Medicine, Technical University of Munich (TUM), Munich, Germany.
Helmholtz AI and Helmholtz Center Munich, Munich, Germany.
Nat Commun. 2025 Feb 13;16(1):1624. doi: 10.1038/s41467-025-56321-y.
Normative representation learning focuses on understanding the typical anatomical distributions from large datasets of medical scans from healthy individuals. Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that accurately reflect these normative patterns. This capability enables the AI allowing them to effectively detect and correct anomalies in new, unseen pathological data without the need for expert labeling. Traditional anomaly detection methods often evaluate the anomaly detection performance, overlooking the crucial role of normative learning. In our analysis, we introduce novel metrics, specifically designed to evaluate this facet in AI models. We apply these metrics across various generative AI frameworks, including advanced diffusion models, and rigorously test them against complex and diverse brain pathologies. In addition, we conduct a large multi-reader study to compare these metrics to experts' evaluations. Our analysis demonstrates that models proficient in normative learning exhibit exceptional versatility, adeptly detecting a wide range of unseen medical conditions. Our code is available at https://github.com/compai-lab/2024-ncomms-bercea.git .
规范表示学习专注于从健康个体的大型医学扫描数据集中理解典型的解剖分布。生成式人工智能（AI）利用这一特性来合成准确反映这些规范模式的图像。这种能力使人工智能能够有效地检测和纠正新的、未见的病理数据中的异常，而无需专家标注。传统的异常检测方法通常评估异常检测性能，而忽略了规范学习的关键作用。在我们的分析中，我们引入了专门设计用于评估人工智能模型这一方面的新指标。我们将这些指标应用于各种生成式人工智能框架，包括先进的扩散模型，并针对复杂多样的脑部病变对它们进行严格测试。此外，我们进行了一项大型多读者研究，将这些指标与专家评估进行比较。我们的分析表明，精通规范学习的模型表现出非凡的通用性，能够熟练地检测出各种未见的医疗状况。我们的代码可在https://github.com/compai-lab/2024-ncomms-bercea.git获取。
