Out-of-Distribution Detection in Medical Image Segmentation with $$\beta $$-VAE and Likelihood Regret

Z. Hong, Y. Yue, Y. Chen, L. Cong, H. Lin, Y. Luo, M. H. Wang, W. Wang, J. Xu, X. Yang, H. Chen, Z. Li, S. Xie, Out-of-distribution Detection in Medical Image Analysis: A survey (2024). arXiv:2404.18279.

D. Zimmerer et al., “MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images,” in IEEE Transactions on Medical Imaging, vol. 41, no. 10, pp. 2728–2738, Oct. 2022. https://doi.org/10.1109/TMI.2022.3170077

Q.-D. Pham, H. Nguyen-Truong, N. N. Phuong, K. N. A. Nguyen, C. D. T. Nguyen, T. Bui, S. Q. Truong, Segtransvae: Hybrid CNN-Transformer with Regularization for Medical Image Segmentation, in: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022, pp. 1–5. https://doi.org/10.1109/ISBI52829.2022.9761417

B. Lambert, M. Louis, S. Doyle, F. Forbes, M. Dojat, A. Tucholka, Leveraging 3D Information In Unsupervised Brain MRI Segmentation, in: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021, pp. 187–190. https://doi.org/10.1109/ISBI48211.2021.9433894

R. Hassanaly, C. Brianceau, O. Colliot, N. Burgos, Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches, in: Deep Generative Models: Third MICCAI Workshop, DGM4MICCAI 2023, in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Springer-Verlag, Berlin, Heidelberg, 2024, p. 110–120.

M. Mostapha, J. Prieto, V. Murphy, J. Girault, M. Foster, A. Rumple, J. Blocher, W. Lin, J. Elison, J. Gilmore, S. Pizer, M. Styner (2019). Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 127–136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32248-9_15

S. S. Ghosh, R. Dhar, D. S. Marcus, A. Sotiras, Siam-VAE: A hybrid deep learning based anomaly detection framework for automated quality control of head CT scans, in: K. M. Iftekharuddin, W. Chen (Eds.), Medical Imaging 2023: Computer-Aided Diagnosis, Vol. 12465, International Society for Optics and Photonics, SPIE, 2023, p. 124650X. https://doi.org/10.1117/12.2654464

E. T. Nalisnick, A. Matsukawa, Y. W. Teh, D. Görür, B. Lakshminarayanan, Do deep generative models know what they don’t know?, in: 7th International Conference on Learning Representations, ICLR 2019, 2019. https://api.semanticscholar.org/CorpusID:53046534

D. P. Kingma, P. Dhariwal, GLOW: Generative Flow with Invertible 1x1 Convolutions, in: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 31, Curran Associates, Inc., 2018. https://proceedings.neurips.cc/paper_files/paper/2018/file/d139db6a236200b21cc7f752979132d0-Paper.pdf

H. Choi, E. Jang, A. A. Alemi, WAIC, but Why? Generative Ensembles for Robust Anomaly Detection (2019). arXiv:1810.01392.

T. Denouden, R. Salay, K. Czarnecki, V. Abdelzad, B. Phan, S. Vernekar, Improving Reconstruction Autoencoder Out-of-Distribution Detection with Mahalanobis Distance, CoRR abs/1812.02765 (2018). arXiv:1812.02765.

G. Floto, S. Kremer, M. Nica, The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection, in: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023. https://openreview.net/pdf?id=YlGsTZODyjz

J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. Depristo, J. Dillon, B. Lakshminarayanan, Likelihood Ratios for Out-of-Distribution Detection, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., 2019. https://proceedings.neurips.cc/paper_files/paper/2019/file/1e79596878b2320cac26dd792a6c51c9-Paper.pdf

S. Ramakrishna, Z. Rahiminasab, G. Karsai, A. Easwaran, A. Dubey. 2022. Efficient Out-of-Distribution Detection Using Latent Space of \(\beta \)–VAE for Cyber-Physical Systems. ACM Trans. Cyber-Phys. Syst. 6, 2, Article 15 (April 2022), 34 pages. https://doi.org/10.1145/3491243

Z. Xiao, Q. Yan, Y. Amit, Likelihood Regret: an Out-of-Distribution detection score for Variational Autoencoder, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Curran Associates Inc., Red Hook, NY, USA, 2020.

A. Vasiliuk, D. Frolova, M. Belyaev, B. Shirokikh, Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation, Journal of Imaging 9 (2023) 191. https://doi.org/10.3390/jimaging9090191.

A. Vasiliuk, D. Frolova, M. Belyaev, B. Shirokikh, Redesigning Out-of-Distribution Detection on 3D Medical Images, in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 5th International Workshop, UNSURE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings, Springer-Verlag, Berlin, Heidelberg, 2023, pp. 126–135.

J. Bao, H. Sun, H. Deng, Y. He, Z. Zhang, X. Li, BMAD: Benchmarks for Medical Anomaly Detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4042–4053.

M. Gutbrod, D. Rauber, D. W. Nunes, C. Palm, OpenMIBOOD: Open medical imaging benchmarks for out-of-distribution detection, in: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 25874–25886.

D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, R. L. Buckner, Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, Journal of Cognitive Neuroscience 19 (9) (2007) 1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498.

T. A. Fahim, F. B. Alam, M. A. Hossain, Brain tumor detection, classification and segmentation by deep learning models from MRI images: Recent approaches, challenges and future directions, Array, Volume 28, (2025), 100571, ISSN 2590-0056, https://doi.org/10.1016/j.array.2025.100571.

S. N. Marimont, V. Siomos, G. Tarroni, MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution detection in medical images, in: A. Mukhopadhyay, I. Oksuz, S. Engelhardt, D. Zhu, Y. Yuan (Eds.), Deep Generative Models, Springer Nature Switzerland, Cham, 2024, pp. 35–44.

E. M. C. Huijben, S. Amirrajab, J. P. W. Pluim. Enhancing reconstruction-based out-of-distribution detection in brain MRI with model and metric ensembles. Comput Methods Programs Biomed. 2025 Dec;272:109045. Epub 2025 Sep 1. PMID: 40915097. https://doi.org/10.1016/j.cmpb.2025.109045.

R. Mehta, A. Filos, U. Baid, C. Sako, R. McKinley, M. Rebsamen, K. Dätwyler, R. Meier, P. Radojewski, G. K. Murugesan, et al., QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation–Analysis of Ranking Scores and Benchmarking Results, Machine Learning for Biomedical Imaging 1 (2022) 1–24. https://www.melba-journal.org/pdf/2022:026.pdf

S. N. Marimont, G. Tarroni, Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies (2023). arXiv:2308.01412.

D. Karimi, A. Gholipour, Improving Calibration and Out-of-Distribution Detection in Deep Models for Medical Image Segmentation, IEEE Transactions on Artificial Intelligence 4 (2) (2023) 383–397. https://doi.org/10.1109/TAI.2022.3159510

J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, NIPS ’20, Curran Associates Inc., Red Hook, NY, USA, 2020.

D. J. Rezende, S. Mohamed, Variational inference with normalizing flows, ICML’15, JMLR.org, 2015, p. 1530–1538.

P. Bevandic, I. Kreso, M. Orsic, S. Segvic, Discriminative Out-Of-Distribution detection for semantic segmentation, ArXiv abs/1808.07703 (2018). https://api.semanticscholar.org/CorpusID:52902638

C. Blundell, J. Cornebise, K. Kavukcuoglu, D. Wierstra, Weight uncertainty in neural networks, in: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, Vol. 37 of ICML’15, JMLR.org, 2015, pp. 1613–1622.

B. Lakshminarayanan, A. Pritzel, C. Blundell, Simple and scalable predictive uncertainty estimation using deep ensembles, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, Curran Associates Inc., Red Hook, NY, USA, 2017, p. 6405–6416.

Y. Gal, Z. Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, in: M. F. Balcan, K. Q. Weinberger (Eds.), Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, PMLR, New York, New York, USA, 2016, pp. 1050–1059. https://proceedings.mlr.press/v48/gal16.html

K. Zadorozhny, P. Thoral, P. Elbers, G. Cinà. (2022). Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation, Multimodal AI in Healthcare: Studies in Computational Intelligence, vol 1060, pp 137–153, Springer, Cham. https://doi.org/10.1007/978-3-031-14771-5_10.

S. Chatterjee, A. Sciarra, M. Dünnwald, P. Tummala, S. K. Agrawal, A. Jauhari, A. Kalra, S. Oeltze-Jafra, O. Speck, A. Nürnberger, Strega: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder, Computers in Biology and Medicine, Volume 149, 2022, 106093, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2022.106093.

D. Zimmerer, J. Petersen, S. A. Kohl, K. H. Maier-Hein, A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients, in: Medical Imaging meets NeurIPS Workshop 2018, Montreal, Canada, 2018, pp. 881–891.

Y. Cai, W. Zhang, H. Chen, K.-T. Cheng, MedIAnomaly: A comparative study of anomaly detection in medical images, Medical Image Analysis, Volume 102, 2025, 103500, ISSN 1361-8415, https://doi.org/10.1016/j.media.2025.103500.

X. Ran, M. Xu, L. Mei, Q. Xu, Q. Liu, Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation, Neural Networks, Volume 145, 2022, pp. 199–208, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2021.10.020.

E. T. Nalisnick, A. Matsukawa, Y. W. Teh, D. Görür, B. Lakshminarayanan, Hybrid models with deep and invertible features, in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 4723–4732. http://proceedings.mlr.press/v97/nalisnick19b.html

D. Hafner, D. Tran, T. P. Lillicrap, A. Irpan, J. Davidson, Noise Contrastive Priors for Functional Uncertainty, in: Conference on Uncertainty in Artificial Intelligence, 2019. https://api.semanticscholar.org/CorpusID:267896905

D. Zimmerer, S. A. A. Kohl, J. Petersen, F. Isensee, K. H. Maier-Hein, Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection (2018). arXiv:1812.05941.

J. Havtorn, J. Frellsen, S. Hauberg, L. Maaløe, Hierarchical VAEs know what they don’t know, in: Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, International Machine Learning Society (IMLS), 2021. https://icml.cc/Conferences/2021

L. Maaløe, M. Fraccaro, V. Liévin, O. Winther, BIVA: A very deep hierarchy of latent variables for generative modeling, Curran Associates Inc., Red Hook, NY, USA, 2019.

Y. Bengio, A. Courville, P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013) 1798–1828. https://doi.org/10.1109/TPAMI.2013.50

I. Higgins, L. Matthey, A. Pal, C. P. Burgess, X. Glorot, M. M. Botvinick, S. Mohamed, A. Lerchner, Beta-VAE: Learning basic visual concepts with a constrained variational framework, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017.

M. Jakab, L. Hudec, W. Benesova, Partial Disentanglement of Hierarchical Variational Auto-Encoder for Texture Synthesis, IET Computer Vision 14 (04 2020). https://doi.org/10.1049/iet-cvi.2019.0416

E. Mathieu, T. Rainforth, N. Siddharth, Y. W. Teh, Disentangling Disentanglement in Variational Autoencoders, in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, Vol. 97 of Proceedings of Machine Learning Research, PMLR, Long Beach, California, USA, 2019, pp. 4402–4412.

W. H. Pinaya, P.-D. Tudosiu, R. Gray, G. Rees, P. Nachev, S. Ourselin, M. J. Cardoso, Unsupervised brain imaging 3d anomaly detection and segmentation with transformers, Medical Image Analysis 79 (2022) 102475. https://doi.org/10.1016/j.media.2022.102475. https://www.sciencedirect.com/science/article/pii/S1361841522001220

M. S. Graham, P.-D. Tudosiu, P. Wright, W. H. L. Pinaya, P. Teikari, A. Patel, J.-M. U-King-Im, Y. H. Mah, J. T. Teo, H. R. Jäger, D. Werring, G. Rees, P. Nachev, S. Ourselin, M. J. Cardoso, Latent transformer models for out-of-distribution detection, Medical Image Analysis 90 (2023) 102967.

L. Abdi, F. Caetano, A. Valiuddin, C. Viviers, H. Joudeh, F. van der Sommen, Out-of-distribution detection in medical imaging via diffusion trajectories, in: C. H. Sudre, M. I. Hoque, R. Mehta, C. Ouyang, C. Qin, M. Rakic, W. M. Wells (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Springer Nature Switzerland, Cham, 2026, pp. 91–101.

M. S. Graham, W. H. L. Pinaya, P. Wright, P.-D. Tudosiu, Y. H. Mah, J. T. Teo, H. R. Jäger, D. Werring, P. Nachev, S. Ourselin, M. J. Cardoso, Unsupervised 3D Out-of-Distribution Detection with Latent Diffusion Models, in: H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Springer Nature Switzerland, Cham, 2023, pp. 446–456.

J. Wyatt, A. Leach, S. M. Schmon, C. G. Willcocks, Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022, pp. 649–655.

A. Heng, A. H. Thiery, and H. Soh, “Out-of-Distribution Detection with a Single Unconditional Diffusion Model,” in Advances in Neural Information Processing Systems (NeurIPS), 2024.

S. Naval Marimont, V. Siomos, M. Baugh, C. Tzelepis, B. Kainz, G. Tarroni, Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection, Springer-Verlag, Berlin, Heidelberg, 2024, p. 243–253.

Y. Shi, A. Abulizi, H. Wang, K. Feng, N. Abudukelimu, Y. Su, H. Abudukelimu, Diffusion models for medical image computing: A survey, Tsinghua Science and Technology 30 (1) (2025) 357–383.

J. Wolleb, F. Bieder, R. Sandkühler, P. C. Cattin, Diffusion models for medical anomaly detection, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII, Springer-Verlag, Berlin, Heidelberg, 2022, p. 35–45.

P. Kirichenko, P. Izmailov, A. G. Wilson, Why normalizing flows fail to detect out-of-distribution data, NIPS ’20, Curran Associates Inc., Red Hook, NY, USA, 2020.

Y. Zhao, Q. Ding, X. Zhang, AE‑FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection, in: International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 1–5, 2023, 2023.

D. Lotfi, M.-A. N. Mahani, M. Koohi-Moghadam, K. T. Bae, Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows (2025). arXiv:2502.11638.

S. F. Razavi, M. Mehmanchi, R. Hosseini, M. Tavassolipour, Out-of-distribution detection using normalizing flows on the data manifold, Applied Intelligence 55 (7) (Apr. 2025).

H. Anthony, K. Kamnitsas, On the Use of Mahalanobis Distance for Out-of-distribution Detection with Neural Networks for Medical Imaging, in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 5th International Workshop, UNSURE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings, Springer-Verlag, Berlin, Heidelberg, 2023, p. 136–146.

S. S. Bakas, BraTS MICCAI Brain tumor dataset (2020). https://doi.org/10.21227/hdtd-5j88

B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, K. Van Leemput, The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS), IEEE Transactions on Medical Imaging 34 (10) (2015) 1993 – 2024. https://doi.org/10.1109/TMI.2014.2377694

S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, J. Freymann, K. Farahani, C. Davatzikos, Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features, Scientific Data 4 (09 2017). https://doi.org/10.1038/sdata.2017.117

S. Bakas, M. Reyes, A. J. et al., Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2019). arXiv:1811.02629.

P. Cui, J. Wang, Out-Of-Distribution (OOD) Detection Based on Deep Learning: A Review, Electronics 11 (21) (2022). https://doi.org/10.3390/electronics11213500. https://www.mdpi.com/2079-9292/11/21/3500

H. Roth, A. Farag, E. B. Turkbey, L. Lu, J. Liu, R. M. Summers, Data From Pancreas-CT (Version 2) [Data set]. The Cancer Imaging Archive (2016). https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU. https://www.cancerimagingarchive.net/collection/pancreas-ct/

B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al., The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS), https://www.med.upenn.edu/cbica/brats2019.html (2019).

B. Wu, Y. Xie, Z. Zhang, J. Ge, K. Yaxley, S. Bahadir, Q. Wu, Y. Liu, M.-S. To, BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset, in: X. Cao, X. Xu, I. Rekik, Z. Cui, X. Ouyang (Eds.), Machine Learning in Medical Imaging, Springer Nature Switzerland, Cham, 2024, pp. 147–156.

M. R. Hernandez Petzsche, E. de la Rosa, U. Hanning, R. Wiest, W. E. Valenzuela, M. Reyes, M. I. Meyer, S.-L. Liew, F. Kofler, I. Ezhov, D. Robben, A. Hutton, T. Friedrich, T. Zarth, J. Bürkle, T. A. Baran, B. Menze, G. Broocks, L. Meyer, C. Zimmer, B. Wiestler, J. S. Kirschke, ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset, Scientific Data 9 (1) (2022) 762. https://doi.org/10.1038/s41597-022-01875-5. https://www.nature.com/articles/s41597-022-01875-5

M. Yuan, Y. Xia, H. Dong, Z. Chen, J. Yao, M. Qiu, K. Yan, X. Yin, Y. Shi, X. Chen, Z. Liu, B. Dong, J. Zhou, L. Lu, L. Zhang, L. Zhang, Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization , in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Los Alamitos, CA, USA, 2023, pp. 23879–23889.

G. Di Biase, H. Blum, R. Siegwart, C. Cadena, Pixel-wise anomaly detection in complex driving scenes, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 16913–16922. https://doi.org/10.1109/CVPR46437.2021.01664

R. Chan, M. Rottmann, H. Gottschalk, Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5108–5117. https://doi.org/10.1109/ICCV48922.2021.00508

D. P. Kingma, M. Welling, Auto-Encoding Variational Bayes, in: 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014. arXiv:http://arxiv.org/abs/1312.6114v10.

A. Kebaili, J. Lapuyade-Lahorgue, S. Ruan, Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review, Journal of Imaging 9 (2023) 81. https://doi.org/10.3390/jimaging9040081

Z. Rahiminasab et al. “Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder.” 2022 6th International Conference on System Reliability and Safety (ICSRS) (2022): 169–178.

E. M. Yu, J. E. Iglesias, A. V. Dalca, M. R. Sabuncu, An Autoencoder Strategy for Adaptive Image Segmentation, in: T. Arbel, I. Ben Ayed, M. de Bruijne, M. Descoteaux, H. Lombaert, C. Pal (Eds.), Proceedings of the Third Conference on Medical Imaging with Deep Learning, Vol. 121 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 881–891. https://proceedings.mlr.press/v121/yu20a.html

C. Lyu, H. Shu. A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation. Brainlesion. 2020 Oct;2020:435-447. Epub 2021 Mar 27. PMID: 36037049; PMCID: PMC9419250. https://doi.org/10.1007/978-3-030-72084-1_39.

T. Lin, P. Goyal, R. B. Girshick, K. He, P. Dollár, Focal Loss for Dense Object Detection, in: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, IEEE Computer Society, 2017, pp. 2999–3007. https://doi.org/10.1109/ICCV.2017.324

J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, J. Luque, Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. https://openreview.net/forum?id=SyxIWpVYvr

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, Berlin, Heidelberg, 2006.

K. Zou, X. Yuan, X. Shen, M. Wang, H. Fu, TBraTS: Trusted Brain Tumor Segmentation, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Lecture Notes in Computer Science, September 2022, https://doi.org/10.1007/978-3-031-16452-1_48.

X. Chen, E. Konukoglu, Unsupervised detection of lesions in brain mri using constrained adversarial auto-encoders, arXiv preprint arXiv:1806.04972 (2018).

C. Baur, B. Wiestler, S. Albarqouni, N. Navab, Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images, in: BrainLesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes) 2018, Lecture Notes in Computer Science, vol. 11383, Springer, 2018, pp. 161–169.

C. Baur, B. Wiestler, S. Albarqouni, N. Navab (2020). Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_54.

A. Uwimana, R. Senanayake, Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis, in: proceedings of ICML 2021 Workshop on Adversarial Machine Learning, Springer Nature Switzerland, 2021.

C. González, K. Gotkowski, M. Fuchs, A. Bucher, A. Dadras, R. Fischbach, I. J. Kaltenborn, A. Mukhopadhyay, Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation, Medical Image Analysis 82 (2022) 102596.

H. Anthony, K. Kamnitsas, On the use of mahalanobis distance for out-of-distribution detection with neural networks for medical imaging, in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Springer Nature Switzerland, 2023, pp. 136–146.

F. Behrendt, D. Bhattacharya, R. Mieling, L. Maack, J. Krüger, R. Opfer, A. Schlaefer, Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection , in: proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Vol. LNCS 15011, Springer Nature Switzerland, 2024.

S. G. Armato, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon, E. J. R. van Beek, D. Yankelevitz, A. M. Biancardi, P. H. Bland, M. S. Brown, R. M. Engelmann, G. E. Laderach, D. Max, R. C. Pais, D. P. Qing, R. Y. Roberts, A. R. Smith, A. Starkey, P. Batra, P. Caligiuri, A. Farooqi, G. W. Gladish, C. M. Jude, R. F. Munden, I. Petkovska, L. E. Quint, L. H. Schwartz, B. Sundaram, L. E. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. Vande Casteele, S. Gupte, M. Sallam, M. D. Heath, M. H. Kuhn, E. Dharaiya, R. Burns, D. S. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, B. Y. Croft, L. P. Clarke, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans, Medical Physics 38 (2) (2011) 915–931. https://doi.org/10.1118/1.3528204

S. Morozov, V. Gombolevskiy, A. Elizarov, M. Gusev, V. Novik, S. Prokudaylo, A. Bardin, E. Popov, N. Ledikhova, V. Chernina, I. Blokhin, A. Nikolaev, R. Reshetnikov, A. Vladzymyrskyy, N. Kulberg, A Simplified Cluster Model and a Tool Adapted for Collaborative Labeling of Lung Cancer CT Scans, Computer Methods and Programs in Biomedicine 206 (2021) 106111. https://doi.org/10.1016/j.cmpb.2021.106111.

C. Chadebec, S. Allassonnière, A geometric perspective on variational autoencoders, in: Advances in Neural Information Processing Systems, Vol. 35, 2022. https://proceedings.neurips.cc/paper/2022/hash/7bf1dc45f850b8ae1b5a1dd4f475f8b6-Abstract.html

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