Abstract:Computed tomography (CT) scanning provides valuable material for detecting hepatic lesions in the liver. Manual detection of hepatic lesions is laborious and heavily relies on the expertise of physicians. Existing algorithms for liver lesion detection exhibit suboptimal performance in detecting subtle lesions. To address this issue, this study proposes a self-supervised liver lesion detection algorithm based on frequency-aware image restoration. Firstly, this algorithm designs a self-supervised task based on synthetic anomalies to generate a broader and more suitable set of pseudo-anomalous images, thereby alleviating the issue of insufficient abnormal data during model training. Secondly, to suppress the sensitivity of the reconstructed network to synthetic liver anomalies, a module is designed to extract high-frequency information from images. By restoring the images from their high-frequency components, the adverse generalization of the reconstructed network to anomalies is mitigated. Lastly, the algorithm adopts weight decay to train the segmented sub-networks, reducing the occurrence of trivial solutions during the early stages of training and enabling the detection of local and subtle lesions. Extensive experiments conducted on publicly available real datasets demonstrate that the proposed method achieves state-of-the-art performance in liver lesion detection.