@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})

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  author={Ruff, Lukas and Kauffmann, Jacob R and Vandermeulen, Robert A and Montavon, Gr{\'e}goire and Samek, Wojciech and Kloft, Marius and Dietterich, Thomas G and M{\"u}ller, Klaus-Robert},
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  year={2021},
  publisher={IEEE},
doi = {10.1109/JPROC.2021.3052449}
}
@inproceedings{bercea2023aes-anon,
  title={***},
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  organization={**}
}
@inproceedings{bercea2023aes,
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  author={Bercea, Cosmin I and Rueckert, Daniel and Schnabel, Julia A},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
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  doi = {10.1007/978-3-031-43904-9_30},
  year={2023},
  organization={Springer}
}
@article{koikkalainen2011multi,
  title={Multi-template tensor-based morphometry: application to analysis of {A}lzheimer's {D}isease},
  author={Koikkalainen, Juha and L{\"o}tj{\"o}nen, Jyrki and Thurfjell, Lennart and Rueckert, Daniel and Waldemar, Gunhild and Soininen, Hilkka and Alzheimer's Disease Neuroimaging Initiative and others},
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@article{bercea2022we,
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}
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year = {2014},
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doi = {10.1016/j.sigpro.2013.12.026},
url = {https://www.sciencedirect.com/science/article/pii/S016516841300515X},
author = {Marco A.F. Pimentel and David A. Clifton and Lei Clifton and Lionel Tarassenko},
keywords = {Novelty detection, One-class classification, Machine learning},
abstract = {Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as “one-class classification”, in which a model is constructed to describe “normal” training data. The novelty detection approach is typically used when the quantity of available “abnormal” data is insufficient to construct explicit models for non-normal classes. Application includes inference in datasets from critical systems, where the quantity of available normal data is very large, such that “normality” may be accurately modelled. In this review we aim to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.}
}
@misc{gong2019memorizing,
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      year={2019},
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      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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title = {The {A}lzheimer's {D}isease {N}euroimaging {I}nitiative ({ADNI}): {MRI} methods},
journal = {Journal of Magnetic Resonance Imaging},
volume = {27},
number = {4},
pages = {685-691},
keywords = {MRI, Alzheimer's disease, clinical trials, imaging methods, imaging standardization},
doi = {10.1002/jmri.21049},


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}
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pages = {},
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journal = {Current {M}edicinal {C}hemistry},
doi = {10.2174/0929867323666161213101126}
}
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}
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  title={Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening},
  author={Bercea, Cosmin I and Wiestler, Benedikt and Rueckert, Daniel and Schnabel Julia},
  year={2023},
  journal={International Conference on Medical Imaging with Deep Learning},
}
@article{voxelmorph,
	doi = {10.1109/tmi.2019.2897538},
  


  
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  
	volume = {38},
  
	number = {8},
  
	pages = {1788--1800},
  
	author = {Guha Balakrishnan and Amy Zhao and Mert R. Sabuncu and John Guttag and Adrian V. Dalca},
  
	title = {{VoxelMorph}: A Learning Framework for Deformable Medical Image Registration},
  
	journal = {{IEEE} Transactions on Medical Imaging}}
@misc{rombach2022highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2022},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{Hoopes_2022,
	doi = {10.59275/j.melba.2022-74f1},
  
	url = {10.59275%2Fj.melba.2022-74f1},
  
	year = 2022,
  
	publisher = {Machine Learning for Biomedical Imaging},
  
	volume = {1},
  
	number = {{IPMI} 2021},
  
	pages = {1--30},
  
	author = {Andrew Hoopes and Malte Hoffmann and Douglas N. Greve and Bruce Fischl and John Guttag and Adrian V. Dalca},
  
	title = {Learning the Effect of Registration Hyperparameters with {HyperMorph}
},
  
	journal = {Machine Learning for Biomedical Imaging}
}
@article{supervised1,
title = {Diagnosis of {A}lzheimer's {D}isease based on regional attention with s{MRI} gray matter slices},
journal = {Journal of Neuroscience Methods},
volume = {365},
pages = {109376},
year = {2022},
issn = {0165-0270},
doi = {10.1016/j.jneumeth.2021.109376},

author = {Yanteng Zhang and Qizhi Teng and Yuyang Liu and Yan Liu and Xiaohai He},
keywords = {Deep learning, Attention mechanism, Alzheimer's disease, SMRI gray matter slice}
}

@ARTICLE{supervised2,
  author={Li, Hengxin and Shi, Xiaoshuang and Zhu, Xiaofeng and Wang, Shuihua and Zhang, Zheng},
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  doi={10.1109/TETCI.2022.3183679}}
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year = {2017},
pages = {},
title = {Residual and Plain Convolutional Neural Networks for 3{D} Brain {MRI} Classification},
doi = {10.1109/ISBI.2017.7950647}
}
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title = {Convolutional neural networks for classification of {A}lzheimer's {D}isease: Overview and reproducible evaluation},
journal = {Medical Image Analysis},
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year = {2020},
issn = {1361-8415},
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keywords = {Convolutional neural network Reproducibility, {A}lzheimer's disease classification Magnetic resonance imaging},

}

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}
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}
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keywords = {spacecrafts, novelty detection, nonlinear, fault detection, dimensionality reduction, denoising autoencoder, autoencoder, auto-assosiative neural network, anomaly detection},
location = {Gold Coast, Australia QLD, Australia},
series = {MLSDA'14}
}

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}
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@misc{adversarial,
      title={Generative Adversarial Networks}, 
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}
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title = {2024 {A}lzheimer's {D}isease facts and figures},
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@InProceedings{zimmerer_uad,
author="Zimmerer, David and Isensee, Fabian
and Petersen, Jens
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and Maier-Hein, Klaus",
title="Unsupervised Anomaly Localization Using Variational Auto-Encoders",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="289--297",
doi="10.1007/978-3-030-32251-9_32"
}

