Fetching the paper…
Reading the bibliography…
Early and accurate disease detection is crucial for patient management and successful treatment outcomes.
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
2013
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.
Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A.V., Criminisi, A., Rueckert, D., Glocker, B.: DeepMedic for brain tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention BrainLes Workshop. pp. 138–149 (2016)
2016
Earlier work this paper cites.
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36
2017
Earlier work this paper cites.
Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. In: International Conference on Medical Imaging with Deep Learning (2018)
2018
Earlier work this paper cites.
Pawlowski, N., Lee, M.C., Rajchl, M., McDonagh, S., Ferrante, E., Kamnitsas, K., Cooke, S., Stevenson, S., Khetani, A., Newman, T., et al.: Unsupervised lesion detection in brain CT using Bayesian convolutional autoencoders. International Conference on Medical Imaging with Deep Learning (2018)
2018
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD — a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9584–9592 (2019)
2019
Earlier work this paper cites.
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)
2019
Earlier work this paper cites.
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54
2019
Cited alongside, same era.
You, S., Tezcan, K.C., Chen, X., Konukoglu, E.: Unsupervised lesion detection via image restoration with a normative prior. In: International Conference on Medical Imaging with Deep Learning. pp. 540–556. PMLR (2019)
2019
Cited alongside, same era.
Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., Maier-Hein, K.: Unsupervised anomaly localization using variational auto-encoders. In: Medical Image Computing and Computer Assisted Intervention. pp. 289–297. Springer (2019)
2019
Cited alongside, same era.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4183–4192 (2020)
2020
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14902–14912 (2021)
2021
Later among the works it cites.
Bercea, C.I., Wiestler, B., Rueckert, D., Albarqouni, S.: Federated disentangled representation learning for unsupervised brain anomaly detection. Nature Machine Intelligence 4
2022
Later among the works it cites.
Kascenas, A., Pugeault, N., O’Neil, A.Q.: Denoising autoencoders for unsupervised anomaly detection in brain MRI. In: International Conference on Medical Imaging with Deep Learning (2022)
2022
Later among the works it cites.
Liew, S.L., Lo, B.P., ., Miarnda R. Donnelly, e.a.: A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Scientific Data 9
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Mao, Y., Xue, F.F., Wang, R., Zhang, J., Zheng, W.S., Liu, H.: Abnormality detection in chest X-ray images using uncertainty prediction autoencoders. In: Medical Image Computing and Computer Assisted Intervention. pp. 529–538. Springer (2020)
2020
Cited alongside, same era.
Schirrmeister, R., Zhou, Y., Ball, T., Zhang, D.: Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Daniel, T., Tamar, A.: Soft-IntroVAE: Analyzing and improving the introspective variational autoencoder. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4391–4400 (2021)
2021
Cited alongside, same era.
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: Pattern Recognition. ICPR International Workshops and Challenges. pp. 475–489. Springer (2021)
2021
Cited alongside, same era.
Ruff, L., Kauffmann, J.R., Vandermeulen, R.A., Montavon, G., Samek, W., Kloft, M., Dietterich, T.G., Müller, K.R.: A unifying review of deep and shallow anomaly detection. Proc. IEEE (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14318–14328 (2022)
2022
Later among the works it cites.
Wyatt, J., Leach, A., Schmon, S.M., Willcocks, C.G.: Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 650–656 (June 2022)
2022
Later among the works it cites.
Zeng, Y., Fu, J., Chao, H., Guo, B.: Aggregated contextual transformations for high-resolution image inpainting. IEEE Transactions on Visualization and Computer Graphics (2022)
2022
Later among the works it cites.
Bercea, C.I., Wiestler, B., Rueckert, D., A, S.J.: Generalizing unsupervised anomaly detection: Towards unbiased pathology screening. International Conference on Medical Imaging with Deep Learning (2023)
2023
Closest in time.