Abstract
Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scenarios, for example, in the case of rare diseases. Reconstruction-based unsupervised anomaly detection, in particular using diffusion models, has gained popularity in the medical field as it allows for training on healthy images alone, eliminating the need for large disease-specific cohorts. These methods assume that a model trained on normal data cannot accurately represent or reconstruct anomalies. However, this assumption often fails with models failing to reconstruct healthy tissue or accurately reconstruct abnormal regions i.e., failing to remove anomalies. In this work, we introduce a novel conditional diffusion model framework for anomaly detection and healthy image reconstruction in brain MRI. Our weakly supervised approach integrates synthetically generated pseudo-pathology images into the modeling process to better guide the reconstruction of healthy images. To generate these pseudo-pathologies, we apply fluid-driven anomaly randomization to augment real pathology segmentation maps from an auxiliary dataset, ensuring that the synthetic anomalies are both realistic and anatomically coherent. We evaluate our model's ability to detect pathology, using both synthetic anomaly datasets and real pathology from the ATLAS dataset. In our extensive experiments, our model: (i) consistently outperforms variational autoencoders, and conditional and unconditional latent diffusion; and (ii) surpasses on most datasets, the performance of supervised inpainting methods with access to paired diseased/healthy images.
Abstract (translated)
监督式机器学习已经在脑部MRI中实现了准确的病理检测，但需要来自患病个体的数据进行训练，在某些情况下这些数据可能难以获得，例如在罕见病的情况下。基于重建的无监督异常检测方法，特别是使用扩散模型的方法，在医学领域变得越来越受欢迎，因为它允许仅通过健康图像进行训练，从而消除了对特定疾病的大规模患者群体的需求。然而，这种方法假设一个经过正常数据训练的模型无法准确表示或重构异常情况，这在实践中经常不成立，因为模型可能无法精确地重建健康的组织或者准确地重构异常区域。 在这项工作中，我们引入了一个新颖的条件扩散模型框架用于脑部MRI中的异常检测和健康图像重构。我们的弱监督方法将通过合成生成伪病理图像来更好地指导健康图像的重构过程。为了生成这些伪病理图像，我们将流体驱动的异常随机化应用于辅助数据集中真实病理分割图，确保合成的异常既现实又解剖结构上一致。 我们在使用合成异常数据集和ATLAS数据集中的真实病理学的情况下评估了我们模型的异常检测能力。在广泛的实验中，我们的模型：（i）始终优于变分自编码器、条件和非条件潜在扩散方法；并且（ii）在大多数数据集中超过了那些可以访问患病/健康图像配对的监督式图像修复方法的表现。 通过这种方式，该研究不仅提高了无监督异常检测技术的有效性，还为处理罕见疾病的MRI分析提供了新的途径。
URL
https://arxiv.org/abs/2506.10233
