  Anomaly Detection (AD) on medical images enables a model to recognize any
type of anomaly pattern without lesion-specific supervised learning. Data
augmentation based methods construct pseudo-healthy images by "pasting" fake
lesions on real healthy ones, and a network is trained to predict healthy
images in a supervised manner. The lesion can be found by difference between
the unhealthy input and pseudo-healthy output. However, using only manually
designed fake lesions fail to approximate to irregular real lesions, hence
limiting the model generalization. We assume by exploring the intrinsic data
property within images, we can distinguish previously unseen lesions from
healthy regions in an unhealthy image. In this study, we propose an Adaptive
Fourier Space Compression (AFSC) module to distill healthy feature for AD. The
compression of both magnitude and phase in frequency domain addresses the hyper
intensity and diverse position of lesions. Experimental results on the BraTS
and MS-SEG datasets demonstrate an AFSC baseline is able to produce promising
detection results, and an AFSC module can be effectively embedded into existing
AD methods.
