Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images
This addresses the challenge of detecting diverse pathological anomalies in medical imaging without extensive annotated data, though it is incremental as it builds on existing autoencoder and pseudo-abnormal techniques.
The authors tackled unsupervised anomaly detection in medical images by training a masked autoencoder on healthy samples and using a pseudo-abnormal module to generate synthetic anomalies for classifier training, achieving competitive results on BRATS2020 and LUNA16 datasets compared to four state-of-the-art methods.
Pathological anomalies exhibit diverse appearances in medical imaging, making it difficult to collect and annotate a representative amount of data required to train deep learning models in a supervised setting. Therefore, in this work, we tackle anomaly detection in medical images training our framework using only healthy samples. We propose to use the Masked Autoencoder model to learn the structure of the normal samples, then train an anomaly classifier on top of the difference between the original image and the reconstruction provided by the masked autoencoder. We train the anomaly classifier in a supervised manner using as negative samples the reconstruction of the healthy scans, while as positive samples, we use pseudo-abnormal scans obtained via our novel pseudo-abnormal module. The pseudo-abnormal module alters the reconstruction of the normal samples by changing the intensity of several regions. We conduct experiments on two medical image data sets, namely BRATS2020 and LUNA16 and compare our method with four state-of-the-art anomaly detection frameworks, namely AST, RD4AD, AnoVAEGAN and f-AnoGAN.