Abstract
Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely AugPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constructed by applying augmentation to randomly selected lesion patches. Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training. Finally, a classification network is trained using the synthetic abnormal samples and the true normal data. Extensive experiments are conducted on two publicly-available medical image datasets with different types of abnormalities. On both datasets, our proposed AugPaste largely outperforms several state-of-the-art unsupervised and semi-supervised anomaly detection methods, and is on a par with the fully-supervised counterpart. To note, AugPaste is even better than the fully-supervised method in detecting early-stage diabetic retinopathy.
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References
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: CVPR (2020)
Burlina, P., Paul, W., Liu, T.A., Bressler, N.M.: Detecting anomalies in retinal diseases using generative, discriminative, and self-supervised deep learning. JAMA Ophthalmol. 140(2), 185–189 (2022)
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6
Graham, B.: Kaggle diabetic retinopathy detection competition report. University of Warwick, pp. 24–26 (2015)
He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., Chao, Y.: The connected-component labeling problem: a review of state-of-the-art algorithms. Pattern Recogn. 70, 25–43 (2017)
Hong, R., Halama, J., Bova, D., Sethi, A., Emami, B.: Correlation of PET standard uptake value and CT window-level thresholds for target delineation in CT-based radiation treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 67(3), 720–726 (2007)
Huang, Y., Lin, L., Cheng, P., Lyu, J., Tang, X.: Lesion-based contrastive learning for diabetic retinopathy grading from fundus images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 113–123. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_11
Kaggle diabetic retinopathy detection competition. https://www.kaggle.com/c/diabetic-retinopathy-detection
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: CutPaste: self-supervised learning for anomaly detection and localization. In: CVPR (2021)
Li, T., et al.: Applications of deep learning in fundus images: a review. Med. Image Anal. 69, 101971 (2021)
Lin, L., Li, M., Huang, Y., Cheng, P., Xia, H., et al.: The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading. Sci. Data 7(1), 1–10 (2020)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Morozov, S.P., Andreychenko, A.E., Pavlov, N.A., Vladzymyrskyy, A.V., Ledikhova, N.V., et al.: MosMedData: chest CT scans with Covid-19 related findings dataset. arXiv preprint (2020). arXiv:2005.06465
Pang, G., Ding, C., Shen, C., Hengel, A.V.D.: Explainable deep few-shot anomaly detection with deviation networks. arXiv preprint (2021). arXiv:2108.00462
Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)
Perera, P.: OCGAN: one-class novelty detection using GANs with constrained latent representations. In: CVPR (2019)
Porwal, P., et al.: Indian diabetic retinopathy image dataset (IDRID): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018)
Quellec, G., Lamard, M., Conze, P.H., Massin, P., Cochener, B.: Automatic detection of rare pathologies in fundus photographs using few-shot learning. Med. Image Anal. 61, 101660 (2020)
Ren, J., et al.: Likelihood ratios for out-of-distribution detection. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: CVPR (2021)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Tian, Yu., Maicas, G., Pu, L.Z.C.T., Singh, R., Verjans, J.W., Carneiro, G.: Few-shot anomaly detection for polyp frames from colonoscopy. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 274–284. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_27
Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)
Zavrtanik, V., Kristan, M., Skočaj, D.: DRAEM-A discriminatively trained reconstruction embedding for surface anomaly detection. In: CVPR (2021)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: ICLR (2018)
Zhou, K., et al.: Encoding structure-texture relation with P-net for anomaly detection in retinal images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 360–377. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_22
Acknowledgement
This study was supported by the Shenzhen Basic Research Program (JCYJ20190809120205578); the National Natural Science Foundation of China (62071210); the Shenzhen Science and Technology Program (RCYX20210609103056042); the Shenzhen Basic Research Program (JCYJ20200925153847004); the Shenzhen Science and Technology Innovation Committee (KCXFZ2020122117340001).
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Huang, W., Huang, Y., Tang, X. (2022). AugPaste: One-Shot Anomaly Detection for Medical Images. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2022. Lecture Notes in Computer Science, vol 13576. Springer, Cham. https://doi.org/10.1007/978-3-031-16525-2_1
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