AFSC: Adaptive Fourier Space Compression for Anomaly Detection
This work addresses anomaly detection in medical imaging, offering a domain-specific improvement over incremental methods by enhancing generalization to irregular real lesions.
The paper tackles the problem of anomaly detection in medical images by proposing an Adaptive Fourier Space Compression (AFSC) module to distill healthy features, addressing limitations of data augmentation methods that use manually designed fake lesions. Experimental results on BraTS and MS-SEG datasets show that AFSC produces promising detection results and can be effectively integrated into existing methods.
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.