Abstract
Anomaly detection using an unsupervised learning scheme has become a challenging research topic. Unsupervised learning requires only unlabeled normal data for training and can detect anomalies in unseen testing data. In this paper, we propose an unsupervised liver lesion detection framework based on generative adversarial networks. We present a new perspective that learning anomalies positively affect learning normal objects (e.g., liver), even if the anomalies are fake. Our framework uses only normal and pseudo-lesions data for training, and the pseudo-lesions data comes from normal data augmentation. We train our framework to predict normal features by transferring normal and augmented data into each other. In addition, we introduce a discriminator network containing a U-Net-like architecture that extracts local and global features effectively for providing more informative feedback to the generator. Further, we also propose a novel reconstruction-error score index based on the image gradient perception pyramid. A higher error-index score indicates a lower similarity between input and output images, which means lesions detected. We conduct extensive experiments on different datasets for liver lesion detection. Our proposed method outperforms other state-of-the-art unsupervised anomaly detection methods.
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Acknowledgements
We would like to thank Profs. Ikuko Nishikawa and Gang Xu of Ritsumeikan University, Japan for their advice on this research. This work was supported in part by the Grant in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant Nos. 20KK0234, 21H03470, and 20K21821, and in part by the Natural Science Foundation of Zhejiang Province (LZ22F020012), in part by Major Scientific Research Project of Zhejiang Lab (2020ND8AD01), and in part by the National Natural Science Foundation of China (82071988), the Key Research and Development Program of Zhejiang Province.
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Li, H., Iwamoto, Y., Han, X., Lin, L., Hu, H., Chen, YW. (2022). An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_21
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