Built on

Otsu, N.: A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9
1979
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
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Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging 34
2014
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Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data 4
2017
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Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 665–674 (2017)
2017
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Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)
Original
2018
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Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X., Konukoglu, E.: Visual feature attribution using wasserstein gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8309–8319 (2018)
2018
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Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain mri using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972 (2018)
Original
2018
Earlier work this paper cites.
Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018)
Original
2018
Earlier work this paper cites.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al.: Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 590–597 (2019)
2019
Earlier work this paper cites.

Similar

Kingma, D.P., Welling, M.: An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691 (2019)
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2019
Cited alongside, same era.
Siddiquee, M.M.R., Zhou, Z., Tajbakhsh, N., Feng, R., Gotway, M.B., Bengio, Y., Liang, J.: Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 191–200 (2019)
2019
Cited alongside, same era.
Arun, N.T., Gaw, N., Singh, P., Chang, K., Hoebel, K.V., Patel, J., Gidwani, M., Kalpathy-Cramer, J.: Assessing the validity of saliency maps for abnormality localization in medical imaging. arXiv preprint arXiv:2006.00063 (2020)
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2020
Cited alongside, same era.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.

Then

Kim, B., Han, I., Ye, J.C.: Diffusemorph: Unsupervised deformable image registration along continuous trajectory using diffusion models. arXiv preprint arXiv:2112.05149 (2021)
Original
2021
Later among the works it cites.
Marimont, S.N., Tarroni, G.: Anomaly detection through latent space restoration using vector quantized variational autoencoders. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 1764–1767. IEEE (2021)
2021
Later among the works it cites.
Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain mri. arXiv preprint arXiv:2109.06023 (2021)
Original
2021
Later among the works it cites.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: Proceedings of the 38th International Conference on Machine Learning. vol. 139, pp. 8162–8171. PMLR (2021)
2021
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2022

Diffusion Models for Medical Anomaly Detection

Wolleb, Julia, Bieder, Florentin, Sandkühler, Robin et al.

Understand

In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training.

Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models.
Those models are often complicated to train or have difficulties to preserve fine details in the image.
We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models.

Built on

Otsu, N.: A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9
1979
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging 34
2014
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data 4
2017
Earlier work this paper cites.
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 665–674 (2017)
2017
Earlier work this paper cites.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)
Original
2018
Earlier work this paper cites.
Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X., Konukoglu, E.: Visual feature attribution using wasserstein gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8309–8319 (2018)
2018
Earlier work this paper cites.
Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain mri using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972 (2018)
Original
2018
Earlier work this paper cites.
Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018)
Original
2018
Earlier work this paper cites.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al.: Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 590–597 (2019)
2019
Earlier work this paper cites.

Similar

Kingma, D.P., Welling, M.: An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691 (2019)
Original
2019
Cited alongside, same era.
Siddiquee, M.M.R., Zhou, Z., Tajbakhsh, N., Feng, R., Gotway, M.B., Bengio, Y., Liang, J.: Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 191–200 (2019)
2019
Cited alongside, same era.
Arun, N.T., Gaw, N., Singh, P., Chang, K., Hoebel, K.V., Patel, J., Gidwani, M., Kalpathy-Cramer, J.: Assessing the validity of saliency maps for abnormality localization in medical imaging. arXiv preprint arXiv:2006.00063 (2020)
Original
2020
Cited alongside, same era.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.

Then

Kim, B., Han, I., Ye, J.C.: Diffusemorph: Unsupervised deformable image registration along continuous trajectory using diffusion models. arXiv preprint arXiv:2112.05149 (2021)
Original
2021
Later among the works it cites.
Marimont, S.N., Tarroni, G.: Anomaly detection through latent space restoration using vector quantized variational autoencoders. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 1764–1767. IEEE (2021)
2021
Later among the works it cites.
Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain mri. arXiv preprint arXiv:2109.06023 (2021)
Original
2021
Later among the works it cites.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: Proceedings of the 38th International Conference on Machine Learning. vol. 139, pp. 8162–8171. PMLR (2021)
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Bhardwaj, P., Singh, V.: A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images. Chaos, Solitons & Fractals 140

2020

Cited alongside, same era.

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

Original

2020

Cited alongside, same era.

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

Original

2020

Cited alongside, same era.

Wolleb, J., Sandkühler, R., Cattin, P.C.: Descargan: Disease-specific anomaly detection with weak supervision. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 14–24. Springer (2020)

2020

Cited alongside, same era.

Choi, J., Kim, S., Jeong, Y., Gwon, Y., Yoon, S.: Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938 (2021)

Original

2021

Cited alongside, same era.

Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34

2021

Cited alongside, same era.

Pinaya, W.H.L., Tudosiu, P.D., Gray, R., Rees, G., Nachev, P., Ourselin, S., Cardoso, M.J.: Unsupervised brain anomaly detection and segmentation with transformers. arXiv preprint arXiv:2102.11650 (2021)

Original

2021