Abstract
The recent success of deep learning relies heavily on the large amount of labeled data. However, acquiring manually annotated symptomatic medical images is notoriously time-consuming and laborious, especially for rare or new diseases. In contrast, normal images from symptom-free healthy subjects without the need of manual annotation are much easier to acquire. In this regard, deep learning based anomaly detection approaches using only normal images are actively studied, achieving significantly better performance than conventional methods. Nevertheless, the previous works committed to develop a specific network for each organ and modality separately, ignoring the intrinsic similarity among images within medical field. In this paper, we propose a model-agnostic framework to detect the abnormalities of various organs and modalities with a single network. By imposing organ and modality classification constraints along with center constraint on the disentangled latent representation, the proposed framework not only improves the generalization ability of the network towards the simultaneous detection of anomalous images with various organs and modalities, but also boosts the performance on each single organ and modality. Extensive experiments with four different baseline models on three public datasets demonstrate the superiority of the proposed framework as well as the effectiveness of each component.
Y. Zhang and D. Lu—Contributed equally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note in this work we use the term ‘normal distribution’ to refer to the distribution of images of normal, healthy subjects, instead of the Gaussian distribution.
References
Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 481–490 (2019)
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39
Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 (2019)
Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705–1714 (2019)
Han, C., et al.: MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform. 22(2), 1–20 (2021)
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475 (2014)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Perera, P., Nallapati, R., Xiang, B.: OCGAN: one-class novelty detection using GANs with constrained latent representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2906 (2019)
Perera, P., Patel, V.M.: Learning deep features for one-class classification. IEEE Trans. Image Process. 28(11), 5450–5463 (2019)
Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5), 1–36 (2018)
Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393–4402. PMLR (2018)
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)
Shehata, M., et al.: Computer-aided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI. IEEE Trans. Biomed. Eng. 66(2), 539–552 (2018)
Tuluptceva, N., Bakker, B., Fedulova, I., Schulz, H., Dylov, D.V.: Anomaly detection with deep perceptual autoencoders. arXiv preprint arXiv:2006.13265 (2020)
Zeng, L.L., et al.: Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30, 74–85 (2018)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhao, J., Zhang, Y., He, X., Xie, P.: COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:2003.13865 (2020)
Zhou, H.Y., Chen, X., Zhang, Y., Luo, R., Wang, L., Yu, Y.: Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports. Nat. Mach. Intell. 4(1), 32–40 (2022)
Zhou, K., et al.: Memorizing structure-texture correspondence for image anomaly detection. IEEE Trans. Neural Netw. Learn. Syst. 2335–2349 (2021)
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2019YFE0113900) and the National Key R &D Program of China under Grant 2020AAA0109500/2020AAA0109501.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., Lu, D., Ning, M., Wang, L., Wei, D., Zheng, Y. (2023). A Model-Agnostic Framework for Universal Anomaly Detection of Multi-organ and Multi-modal Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-43898-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43897-4
Online ISBN: 978-3-031-43898-1
eBook Packages: Computer ScienceComputer Science (R0)