论文标题
无监督的异常定位与结构特征自动编码器
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
论文作者
论文摘要
无监督的异常检测已成为检测医学图像中病理学的一种流行方法，因为它不需要监督或标签进行培训。最常见的是，异常检测模型会生成输入映像的“正常”版本，而Pixel $ l^p $ - 两者的差异用于本地化异常。但是，大多数医学图像中存在的复杂解剖结构的重建不完善，通常是由于不完善的重建而发生的。该方法还无法检测到没有与周围组织的强度差异很大的异常。我们建议使用特征映射功能解决此问题，该功能将输入强度图像转换为具有多个通道的空间，在该空间中可以沿着从原始图像提取的不同判别特征图检测到异常。然后，我们使用结构相似性损失在该空间中训练自动编码器模型，该模型不仅考虑强度差异，而且考虑对比度和结构。我们的方法大大提高了两个大脑MRI的医疗数据集的性能。代码和实验可从https://github.com/felime/feature-autoencoder获得
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the anomaly detection model generates a "normal" version of an input image, and the pixel-wise $l^p$-difference of the two is used to localize anomalies. However, large residuals often occur due to imperfect reconstruction of the complex anatomical structures present in most medical images. This method also fails to detect anomalies that are not characterized by large intensity differences to the surrounding tissue. We propose to tackle this problem using a feature-mapping function that transforms the input intensity images into a space with multiple channels where anomalies can be detected along different discriminative feature maps extracted from the original image. We then train an Autoencoder model in this space using structural similarity loss that does not only consider differences in intensity but also in contrast and structure. Our method significantly increases performance on two medical data sets for brain MRI. Code and experiments are available at https://github.com/FeliMe/feature-autoencoder
