论文标题
医学异常检测的扩散模型
Diffusion Models for Medical Anomaly Detection
论文作者
论文摘要
在医疗应用中，弱监督的异常检测方法引起了极大的兴趣，因为培训只需要图像级注释。当前的异常检测方法主要依赖于生成的对抗网络或自动编码器模型。这些模型通常很复杂，可以在图像中保留细节的精细细节。我们提出了一种基于脱氧扩散隐式模型的新型弱监督的异常检测方法。我们将确定性的迭代和去牙型计划与分类器指导结合在一起，以在患病和健康受试者之间进行图像对图像翻译。我们的方法生成非常详细的异常图，而无需进行复杂的训练程序。我们在BRATS2020数据集上评估了我们的方法，用于脑瘤检测和用于检测胸腔积液的CHEXPERT数据集。
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions.
