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research-article

Deep Learning for Medical Anomaly Detection – A Survey

Authors:

Tharindu Fernando,

Harshala Gammulle,

Sridha Sridharan,

Clinton FookesAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 7

Article No.: 141, Pages 1 - 37

https://doi.org/10.1145/3464423

Published: 18 July 2021 Publication History

Abstract

Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

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References

[1]

David Ahmedt-Aristizabal, Tharindu Fernando, Simon Denman, Lars Petersson, Matthew J. Aburn, and Clinton Fookes. 2020. Neural memory networks for robust classification of seizure type. In International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]

Nsreen Alahmadi, Sergey A. Evdokimov, Yury Juri Kropotov, Andreas M. Müller, and Lutz Jäncke. 2016. Different resting state EEG features in children from Switzerland and Saudi Arabia. Frontiers Hum. Neurosc. 10 (2016), 559.

[3]

Haya Alaskar, Abir Hussain, Nourah Al-Aseem, Panos Liatsis, and Dhiya Al-Jumeily. 2019. Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors 19, 6 (2019), 1265.

[4]

Rabia Ali, Muhammad Umar Karim Khan, and Chong Min Kyung. 2020. Self-supervised representation learning for visual anomaly detection. arXiv preprint arXiv:2006.09654 (2020).

[5]

Ahmed Almazroa, Sami Alodhayb, Essameldin Osman, Eslam Ramadan, Mohammed Hummadi, Mohammed Dlaim, Muhannad Alkatee, Kaamran Raahemifar, and Vasudevan Lakshminarayanan. 2017. Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images. Int. Ophthalm. 37, 3 (2017), 701–717.

[6]

Naomi Altman and Martin Krzywinski. 2015. Association, correlation and causation. Nat. Meth. 12, 1 (2015), 899–900.

[7]

Ralph G. Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E. Elger. 2001. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64, 6 (2001), 061907.

[8]

Jo Aoe, Ryohei Fukuma, Takufumi Yanagisawa, Tatsuya Harada, Masataka Tanaka, Maki Kobayashi, You Inoue, Shota Yamamoto, Yuichiro Ohnishi, and Haruhiko Kishima. 2019. Automatic diagnosis of neurological diseases using MEG signals with a deep neural network. Sci. Rep. 9, 1 (2019), 1–9.

[9]

Mohammad R. Arbabshirani, Brandon K. Fornwalt, Gino J. Mongelluzzo, Jonathan D. Suever, Brandon D. Geise, Aalpen A. Patel, and Gregory J. Moore. 2018. Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Dig. Med. 1, 1 (2018), 1–7.

[10]

Dana H. Ballard. 1987. Modular learning in neural networks. In AAAI Conference on Artificial Intelligence. 279–284.

[11]

Andrew L. Beam and Isaac S. Kohane. 2018. Big data and machine learning in health care. Jama 319, 13 (2018), 1317–1318.

[12]

Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 2 (1994), 157–166.

Digital Library

[13]

Leo Breiman. 2001. Random forests. Mach. Learn. 45, 1 (2001), 5–32.

Digital Library

[14]

Daniel C. Castro, Ian Walker, and Ben Glocker. 2020. Causality matters in medical imaging. Nat. Commun. 11, 1 (2020), 1–10.

[15]

Raghavendra Chalapathy and Sanjay Chawla. 2019. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407 (2019).

[16]

David Charte, Francisco Charte, Salvador García, María J. del Jesus, and Francisco Herrera. 2018. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Inf. Fus. 44 (2018), 78–96.

[17]

Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, Rupali Khatun, Petia Radeva, Georg Rose, Sebastian Stober, Oliver Speck, and Andreas Nürnberger. 2020. Exploration of interpretability techniques for deep COVID-19 classification using chest X-ray images. arXiv preprint arXiv:2006.02570 (2020).

[18]

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N. Balasubramanian. 2018. Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In IEEE Winter Conference on Applications of Computer Vision (WACV’18). IEEE, 839–847.

[19]

Jun Cheng, Wei Huang, Shuangliang Cao, Ru Yang, Wei Yang, Zhaoqiang Yun, Zhijian Wang, and Qianjin Feng. 2015. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS One 10, 10 (2015), e0140381.

[20]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).

[21]

François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In IEEE Conference on Computer Vision and Pattern Recognition. 1251–1258.

[22]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

Digital Library

[23]

Gari D. Clifford, Chengyu Liu, Benjamin Moody, David Springer, Ikaro Silva, Qiao Li, and Roger G. Mark. 2016. Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in cardiology challenge 2016. In Computing in Cardiology Conference (CinC’16). IEEE, 609–612.

[24]

Mark J. Cook, Terence J. O’Brien, Samuel F. Berkovic, Michael Murphy, Andrew Morokoff, Gavin Fabinyi, Wendyl D’Souza, Raju Yerra, John Archer, Lucas Litewka, et al. 2013. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: A first-in-man study. Lancet Neurol. 12, 6 (2013), 563–571.

[25]

Jake Cowton, Ilias Kyriazakis, Thomas Plötz, and Jaume Bacardit. 2018. A combined deep learning GRU-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18, 8 (2018), 2521.

[26]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A Very deep convolutional networks for large-scale image recognition hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248–255.

[27]

Terrance DeVries and Graham W. Taylor. 2018. Leveraging uncertainty estimates for predicting segmentation quality. arXiv preprint arXiv:1807.00502 (2018).

[28]

Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Houman Ghaemmaghami, Sridha Sridharan, and Clinton Fookes. 2020. Domain generalization in biosignal classification. arXiv preprint arXiv:2011.06207 (2020).

[29]

Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan, Houman Ghaemmaghami, and Clinton Fookes. 2020. A robust interpretable deep learning classifier for heart anomaly detection without segmentation. IEEE J. Biomed. Health Inform. 25, 6 (2020), 2162–2171.

[30]

Carl Doersch, Abhinav Gupta, and Alexei A. Efros. 2015. Unsupervised visual representation learning by context prediction. In IEEE International Conference on Computer Vision. 1422–1430.

[31]

Wenju Du, Nini Rao, Dingyun Liu, Hongxiu Jiang, Chengsi Luo, Zhengwen Li, Tao Gan, and Bing Zeng. 2019. Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images. IEEE Access 7 (2019), 142053–142069.

[32]

Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab, and Arash Gharehbaghi. 2020. A review on deep learning methods for ECG arrhythmia classification. Exp. Syst. Applic.: 7, 1 (2020), 100033.

[33]

Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017), 115–118.

[34]

Shanhui Fan, Lanmeng Xu, Yihong Fan, Kaihua Wei, and Lihua Li. 2018. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys. Med. Biol. 63, 16 (2018), 165001.

[35]

Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal, Sridha Sridharan, Kristin R. Laurens, Patrick Johnston, and Clinton Fookes. 2020. Neural memory plasticity for medical anomaly detection. Neural Netw. (2020).

[36]

Tharindu Fernando, Simon Denman, Aaron McFadyen, Sridha Sridharan, and Clinton Fookes. 2018. Tree memory networks for modelling long-term temporal dependencies. Neurocomputing 304 (2018), 64–81.

Digital Library

[37]

Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2018. GD-GAN: Generative adversarial networks for trajectory prediction and group detection in crowds. In Asian Conference on Computer Vision. Springer, 314–330.

[38]

Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2018. Learning temporal strategic relationships using generative adversarial imitation learning. In 17th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 113–121.

[39]

Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2018. Task specific visual saliency prediction with memory augmented conditional generative adversarial networks. In IEEE Winter Conference on Applications of Computer Vision (WACV’18). IEEE, 1539–1548.

[40]

Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2019. Memory augmented deep generative models for forecasting the next shot location in tennis. IEEE Trans. Knowl. Data Eng. 32, 9 (2019), 1785–1797.

Digital Library

[41]

Tharindu Fernando, Houman Ghaemmaghami, Simon Denman, Sridha Sridharan, Nayyar Hussain, and Clinton Fookes. 2019. Heart sound segmentation using bidirectional LSTMs with attention. IEEE J. Biomed. Health Inform. 24, 6 (2019), 1601–1609.

[42]

Kais Gadhoumi, Jean-Marc Lina, and Jean Gotman. 2012. Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clin. Neurophys. 123, 10 (2012), 1906–1916.

[43]

Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2019. Forecasting future action sequences with neural memory networks. In British Machine Vision Conference (BMVC’19).

[44]

Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2020. Fine-grained action segmentation using the semi-supervised action GAN. Pattern Recog. 98 (2020), 107039.

Digital Library

[45]

Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2020. Two-stream deep feature modelling for automated video endoscopy data analysis. In International Conference on Medical Image Computing and Computer Assisted Intervention.

Digital Library

[46]

Shan Gao, Yineng Zheng, and Xingming Guo. 2020. Gated recurrent unit-based heart sound analysis for heart failure screening. BioMed. Engineering OnLine 19, 1 (2020), 3.

[47]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In International Conference on Advances in Neural Information Processing Systems. 2672–2680.

[48]

Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. 2013. Toward supervised anomaly detection. J. Artif. Intell. Res. 46 (2013), 235–262.

[49]

Zhongyi Han, Benzheng Wei, Stephanie Leung, Ilanit Ben Nachum, David Laidley, and Shuo Li. 2018. Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning. Neuroinformatics 16, 3-4 (2018), 325–337.

[50]

Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. 2018. Deep learning approach for automatic classification of ocular and cardiac artifacts in MEG Data. J. Eng. 2018 (2018).

[51]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition. 770–778.

[52]

Geoffrey E. Hinton. 2009. Deep belief networks. Scholarpedia 4, 5 (2009), 5947.

[53]

Sepp Hochreiter. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncert. Fuzz. Knowl.-based Syst. 6, 02 (1998), 107–116.

Digital Library

[54]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.

Digital Library

[55]

Shi Hu, Jakub Tomczak, and Max Welling. 2018. Meta-learning for medical image classification. In Conference on Medical Imaging with Deep Learning (MIDL’18).

[56]

Chengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, and Geyong Min. 2018. Towards experienced anomaly detector through reinforcement learning. In AAAI Conference on Artificial Intelligence.

[57]

Ramy Hussein, Mohamed Osama Ahmed, Rabab Ward, Z. Jane Wang, Levin Kuhlmann, and Yi Guo. 2019. Human intracranial EEG quantitative analysis and automatic feature learning for epileptic seizure prediction. arXiv preprint arXiv:1904.03603 (2019).

[58]

Matthias Ihle, Hinnerk Feldwisch-Drentrup, César A. Teixeira, Adrien Witon, Björn Schelter, Jens Timmer, and Andreas Schulze-Bonhage. 2012. EPILEPSIAE–A European epilepsy database. Comput. Meth. Prog. Biomed. 106, 3 (2012), 127–138.

Digital Library

[59]

Jyoti Islam and Yanqing Zhang. 2018. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inform. 5, 2 (2018), 2.

[60]

Francesca Jacini, Pierpaolo Sorrentino, Anna Lardone, Rosaria Rucco, Fabio Baselice, Carlo Cavaliere, Marco Aiello, Mario Orsini, Alessandro Iavarone, Valentino Manzo, et al. 2018. Amnestic mild cognitive impairment is associated with frequency-specific brain network alterations in temporal poles. Front. Aging Neurosci. 10 (2018), 400.

[61]

Clifford R. Jack Jr, Matt A. Bernstein, Nick C. Fox, Paul Thompson, Gene Alexander, Danielle Harvey, Bret Borowski, Paula J. Britson, Jennifer L. Whitwell, Chadwick Ward, et al. 2008. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Mag. Reson. Imag. 27, 4 (2008), 685–691.

[62]

Abhyuday N. Jagannatha and Hong Yu. 2016. Bidirectional RNN for medical event detection in electronic health records. In Association for Computational Linguistics Conference, Vol. 2016. NIH Public Access, 473.

[63]

Pratik Kumar Jawanpuria, Maksim Lapin, Matthias Hein, and Bernt Schiele. 2015. Efficient output kernel learning for multiple tasks. In International Conference on Advances in Neural Information Processing Systems. 1189–1197.

[64]

Justin M. Johnson and Taghi M. Khoshgoftaar. 2019. Survey on deep learning with class imbalance. J. Big Data 6, 1 (2019), 27.

[65]

Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in Bayesian deep learning for computer vision? In International Conference on Advances in Neural Information Processing Systems. 5574–5584.

[66]

Haidar Khan, Lara Marcuse, Madeline Fields, Kalina Swann, and Bulent Yener. 2018. Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. 65, 9 (9 2018), 2109–2118. TBME.2017.2785401

[67]

Isabell Kiral-Kornek, Subhrajit Roy, Ewan Nurse, Benjamin Mashford, Philippa Karoly, Thomas Carroll, Daniel Payne, Susmita Saha, Steven Baldassano, Terence O’Brien, et al. 2018. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27 (2018), 103–111.

[68]

Pavel Kisilev, Eli Sason, Ella Barkan, and Sharbell Hashoul. 2016. Medical image description using multi-task-loss CNN. In Deep Learning and Data Labeling for Medical Applications. Springer, 121–129.

[69]

Eyal Klang, Yiftach Barash, Reuma Yehuda Margalit, Shelly Soffer, Orit Shimon, Ahmad Albshesh, Shomron Ben-Horin, Marianne Michal Amitai, Rami Eliakim, and Uri Kopylov. 2020. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastroint. Endos. 91, 3 (2020), 606–613.

[70]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In International Conference on Advances in Neural Information Processing Systems. 1097–1105.

[71]

Levin Kuhlmann, Philippa Karoly, Dean R. Freestone, Benjamin H. Brinkmann, Andriy Temko, Alexandre Barachant, Feng Li, Gilberto Titericz Jr, Brian W. Lang, Daniel Lavery, et al. 2018. Epilepsyecosystem. org: Crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141, 9 (2018), 2619–2630.

[72]

Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik. 2020. Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Comput. Statist. Data Anal. 142 (2020), 106816.

Digital Library

[73]

Anna Lardone, Marianna Liparoti, Pierpaolo Sorrentino, Rosaria Rucco, Francesca Jacini, Arianna Polverino, Roberta Minino, Matteo Pesoli, Fabio Baselice, Antonietta Sorriso, et al. 2018. Mindfulness meditation is related to long-lasting changes in hippocampal functional topology during resting state: A magnetoencephalography study. Neural Plastic. 2018 (2018).

[74]

Siddique Latif, Muhammad Usman, Rajib Rana, and Junaid Qadir. 2018. Phonocardiographic sensing using deep learning for abnormal heartbeat detection. IEEE Sens. J. 18, 22 (2018), 9393–9400.

[75]

Minh Hung Le, Jingyu Chen, Liang Wang, Zhiwei Wang, Wenyu Liu, Kwang-Ting Tim Cheng, and Xin Yang. 2017. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys. Med. Biol. 62, 16 (2017), 6497.

[76]

Hyebin Lee, Seong Tae Kim, and Yong Man Ro. 2019. Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. Springer, 21–29.

[77]

Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl. 2017. Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. 7, 1 (2017), 1–14.

[78]

Fan Li, Hong Tang, Shang Shang, Klaus Mathiak, and Fengyu Cong. 2020. Classification of heart sounds using convolutional neural network. Appl. Sci. 10, 11 (2020), 3956.

[79]

Suyi Li, Feng Li, Shijie Tang, and Wenji Xiong. 2020. A review of computer-aided heart sound detection techniques. BioMed Res. Int. 2020 (2020).

[80]

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In IEEE International Conference on Computer Vision. 2980–2988.

[81]

Manuel Lopez-Martin, Angel Nevado, and Belen Carro. 2020. Detection of early stages of Alzheimer’s disease based on MEG activity with a randomized convolutional neural network. Artif. Intell. Med. 107 (2020), 101924.

[82]

Donghuan Lu, Karteek Popuri, Gavin Weiguang Ding, Rakesh Balachandar, and Mirza Faisal Beg. 2018. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8, 1 (2018), 1–13.

[83]

Yuchen Lu and Peng Xu. 2018. Anomaly detection for skin disease images using variational autoencoder. arXiv preprint arXiv:1807.01349 (2018).

[84]

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In International Conference on Advances in Neural Information Processing Systems. 4765–4774.

[85]

Scott M. Lundberg, Bala Nair, Monica S. Vavilala, Mayumi Horibe, Michael J. Eisses, Trevor Adams, David E. Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, et al. 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomed. Eng. 2, 10 (2018), 749–760.

[86]

Alexander Selvikvåg Lundervold and Arvid Lundervold. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29, 2 (2019), 102–127.

[87]

Ying Ma and Jose C. Principe. 2019. A taxonomy for neural memory networks. IEEE Trans. Neural Netw. Learn. Syst. 31, 6 (2019), 1780–1793.

[88]

Kushagra Mahajan, Monika Sharma, and Lovekesh Vig. 2020. Meta-DermDiagnosis: Few-Shot skin disease identification using meta-learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 730–731.

[89]

D. S. Marcus, T. H. Wang, et al. [n.d.]. OASIS: Cross-Sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cog. Neurosci. 19, 9 ([n. d.]), 1498–507.

[90]

Christoph Molnar. 2020. Interpretable Machine Learning. Lulu.com.

[91]

Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller. 2017. Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recog. 65 (2017), 211–222.

Digital Library

[92]

Sankha Subhra Mullick, Shounak Datta, and Swagatam Das. 2019. Generative adversarial minority oversampling. In IEEE International Conference on Computer Vision. 1695–1704.

[93]

Tsendsuren Munkhdalai and Hong Yu. 2017. Neural semantic encoders. In Association for Computational Linguistics Conference, Vol. 1. NIH Public Access, 397.

[94]

Tanya Nair, Doina Precup, Douglas L. Arnold, and Tal Arbel. 2020. Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59 (2020), 101557.

[95]

Meike Nauta, Doina Bucur, and Christin Seifert. 2019. Causal discovery with attention-based convolutional neural networks. Mach. Learn. Knowl. Extract. 1, 1 (2019), 312–340.

[96]

Mehdi Noroozi and Paolo Favaro. 2016. Unsupervised learning of visual representations by solving jigsaw puzzles. In European Conference on Computer Vision. Springer, 69–84.

[97]

Min-hwan Oh and Garud Iyengar. 2019. Sequential anomaly detection using inverse reinforcement learning. In 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1480–1490.

[98]

Shu Lih Oh, V. Jahmunah, Chui Ping Ooi, Ru-San Tan, Edward J. Ciaccio, Toshitaka Yamakawa, Masayuki Tanabe, Makiko Kobayashi, and U. Rajendra Acharya. 2020. Classification of heart sound signals using a novel deep WaveNet model. Comput. Meth. Prog. Biom. 196, 1 (2020), 105604.

[99]

Soumyasundar Pal, Florence Regol, and Mark Coates. 2019. Bayesian graph convolutional neural networks using node copying. arXiv preprint arXiv:1911.04965 (2019).

[100]

Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, and Carlos A. Silva. 2018. Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications. Springer, 106–114.

[101]

Konstantin Pogorelov, Kristin Ranheim Randel, Thomas de Lange, Sigrun Losada Eskeland, Carsten Griwodz, Dag Johansen, Concetto Spampinato, Mario Taschwer, Mathias Lux, Peter Thelin Schmidt, et al. 2017. Nerthus: A bowel preparation quality video dataset. In 8th ACM on Multimedia Systems Conference. 170–174.

Digital Library

[102]

Konstantin Pogorelov, Kristin Ranheim Randel, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, Mathias Lux, Peter Thelin Schmidt, et al. 2017. KVASIR: A multi-class image dataset for computer aided gastrointestinal disease detection. In 8th ACM on Multimedia Systems Conference. 164–169.

Digital Library

[103]

Khansa Rasheed, Adnan Qayyum, Junaid Qadir, Shobi Sivathamboo, Patrick Kwan, Levin Kuhlmann, Terence O’Brien, and Adeel Razi. 2020. Machine learning for predicting epileptic seizures using eeg signals: A review. arXiv preprint arXiv:2002.01925 (2020).

[104]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should I trust you?” Explaining the predictions of any classifier. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144.

Digital Library

[105]

Jonathan G. Richens, Ciarán M. Lee, and Saurabh Johri. 2020. Improving the accuracy of medical diagnosis with causal machine learning. Nature Commun. 11, 1 (2020), 1–9.

[106]

Rosaria Rucco, Marianna Liparoti, Francesca Jacini, Fabio Baselice, Antonella Antenora, Giuseppe De Michele, Chiara Criscuolo, Antonio Vettoliere, Laura Mandolesi, Giuseppe Sorrentino, et al. 2019. Mutations in the SPAST gene causing hereditary spastic paraplegia are related to global topological alterations in brain functional networks. Neurolog. Sci. 40, 5 (2019), 979–984.

[107]

Adam Santoro, David Raposo, David G. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. In International Conference on Advances in Neural Information Processing Systems. 4967–4976.

[108]

Daisuke Sato, Shouhei Hanaoka, Yukihiro Nomura, Tomomi Takenaga, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, and Osamu Abe. 2018. A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes. In Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575. International Society for Optics and Photonics, 105751P.

[109]

Abraham Savitzky and Marcel J. E. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures.Analytical Chem. 36, 8 (1964), 1627–1639.

[110]

Divya Saxena and Jiannong Cao. 2020. Generative adversarial networks (GANs): Challenges, solutions, and future directions. arXiv preprint arXiv:2005.00065 (2020).

[111]

Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth. 2019. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54 (2019), 30–44.

[112]

Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas, Sebastian M. Waldstein, and Hrvoje Bogunović. 2018. Artificial intelligence in retina. Prog. Retin. Eye Res. 67 (2018), 1–29.

[113]

Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, and Ursula Schmidt-Erfurth. 2019. Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT. IEEE Trans. Med. Imag. 39, 1 (2019), 87–98.

[114]

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In IEEE International Conference on Computer Vision. 618–626.

[115]

Mohamed Shehata, Fahmi Khalifa, Ahmed Soliman, Mohammed Ghazal, Fatma Taher, Mohamed Abou El-Ghar, Amy C. Dwyer, Gimel’farb, Robert S. Keynton, and Ayman El-Baz. 2018. Computer-aided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI. IEEE Trans. Biomed. Eng. 66, 2 (2018), 539–552.

[116]

Ali Hossam Shoeb. 2009. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Ph.D. Dissertation. Massachusetts Institute of Technology.

[117]

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. arXiv preprint arXiv:1704.02685 (2017).

Digital Library

[118]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

[119]

Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan. 2020. Explainable deep learning models in medical image analysis. arXiv preprint arXiv:2005.13799 (2020).

[120]

Sanjay P. Singh. 2014. Magnetoencephalography: basic principles. Ann. Ind. Acad. Neurol. 17, Suppl 1 (2014), S107.

[121]

Shelly Soffer, Eyal Klang, Orit Shimon, Noy Nachmias, Rami Eliakim, Shomron Ben-Horin, Uri Kopylov, and Yiftach Barash. 2020. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastroint. Endos. 92, 4 (2020), 831–839.

[122]

Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2014. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806 (2014).

[123]

David B. Springer, Lionel Tarassenko, and Gari D. Clifford. 2015. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans. Biomed. Eng. 63, 4 (2015), 822–832.

[124]

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. arXiv preprint arXiv:1703.01365 (2017).

Digital Library

[125]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition. 1–9.

[126]

D. K. Thara, B. G. PremaSudha, and Fan Xiong. 2019. Epileptic seizure detection and prediction using stacked bidirectional long short term memory. Pattern Recog. Lett. 128 (2019), 529–535.

Digital Library

[127]

Srikanth Thudumu, Philip Branch, Jiong Jin, and Jugdutt Jack Singh. 2020. A comprehensive survey of anomaly detection techniques for high dimensional big data. J. Big Data 7, 1 (2020), 1–30.

[128]

Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Damien Querlioz, Luping Zhou, and Omid Kavehei. 2019. Epileptic seizure forecasting with generative adversarial networks. IEEE Access 7 (2019), 143999–144009.

[129]

Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, and Omid Kavehei. 2017. A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. arXiv preprint arXiv:1707.01976 (2017).

[130]

Nikos Tsiknakis, Eleftherios Trivizakis, Evangelia E. Vassalou, Georgios Z. Papadakis, Demetrios A. Spandidos, Aristidis Tsatsakis, Jose Sánchez-García, Rafael López-González, Nikolaos Papanikolaou, Apostolos H. Karantanas, et al. 2020. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Exper. Therap. Med. 20, 2 (2020), 727–735.

[131]

Kostas M. Tsiouris, Vasileios C. Pezoulas, Michalis Zervakis, Spiros Konitsiotis, Dimitrios D. Koutsouris, and Dimitrios I. Fotiadis. 2018. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput. Biol. Med. 99 (2018), 24–37.

[132]

J. T. Turner, Adam Page, Tinoosh Mohsenin, and Tim Oates. 2017. Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. arXiv preprint arXiv:1708.08430 (2017).

[133]

Hristina Uzunova, Sandra Schultz, Heinz Handels, and Jan Ehrhardt. 2019. Unsupervised pathology detection in medical images using conditional variational autoencoders. Int. J. Comput. Assist. Radiol. Surg. 14, 3 (2019), 451–461.

[134]

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In 25th International Conference on Machine Learning. 1096–1103.

Digital Library

[135]

Kai Wang, Youjin Zhao, Qingyu Xiong, Min Fan, Guotan Sun, Longkun Ma, and Tong Liu. 2016. Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals. Sci. Prog. 2016, 1 2016 (2016).

[136]

Sen Wang, Yuxiang Xing, Li Zhang, Hewei Gao, and Hao Zhang. 2019. A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Phys. Med. Biol. 64, 23 (2019), 235014.

[137]

R. J. Williams and David Zipser. 1995. Gradient-based learning algorithm for recurrent networks. In Backpropagation: Theory, Architectures, and Applications. L. Erlbaum Associates Inc., 433–486.

[138]

M. Winterhalder, T. Maiwald, H. U. Voss, R. Aschenbrenner-Scheibe, J. Timmer, and A. Schulze-Bonhage. 2003. The seizure prediction characteristic: A general framework to assess and compare seizure prediction methods. Epilep. Behav. 4, 3 (2003), 318–325.

[139]

Jimmy Ming-Tai Wu, Meng-Hsiun Tsai, Yong Zhi Huang, S. K. Hafizul Islam, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Giancarlo Fortino. 2019. Applying an ensemble convolutional neural network with Savitzky–Golay filter to construct a phonocardiogram prediction model. Appl. Soft Comput. 78 (2019), 29–40.

Digital Library

[140]

Hangzhou Yang and Huiying Gao. 2018. Toward sustainable virtualized healthcare: extracting medical entities from Chinese online health consultations using deep neural networks. Sustainability 10, 9 (2018), 3292.

[141]

Te-chung Issac Yang and Haowei Hsieh. 2016. Classification of acoustic physiological signals based on deep learning neural networks with augmented features. In Computing in Cardiology Conference (CinC’16). IEEE, 569–572.

[142]

Youngjin Yoo, Lisa Y. W. Tang, Tom Brosch, David K. B. Li, Shannon Kolind, Irene Vavasour, Alexander Rauscher, Alex L. MacKay, Anthony Traboulsee, and Roger C. Tam. 2018. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage: Clin. 17 (2018), 169–178.

[143]

Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, and Sally Shrapnel. 2019. Deep neural network or dermatologist? In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. Springer, 48–55.

[144]

Ling-Li Zeng, Huaning Wang, Panpan Hu, Bo Yang, Weidan Pu, Hui Shen, Xingui Chen, Zhening Liu, Hong Yin, Qingrong Tan, et al. 2018. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30 (2018), 74–85.

[145]

Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, and Jesper Tegnér. 2019. An algorithmic information calculus for causal discovery and reprogramming systems. iScience 19 (2019), 1160–1172.

[146]

Hector Zenil, Narsis A. Kiani, Allan A. Zea, and Jesper Tegnér. 2019. Causal deconvolution by algorithmic generative models. Nat. Mach. Intell. 1, 1 (2019), 58–66.

[147]

Liyuan Zhang, Huamin Yang, and Zhengang Jiang. 2018. Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN. Biomed. Eng. Online 17, 1 (2018), 181.

[148]

Richard Zhang, Phillip Isola, and Alexei A. Efros. 2016. Colorful image colorization. In European Conference on Computer Vision. Springer, 649–666.

[149]

Xi Sheryl Zhang, Fengyi Tang, Hiroko H. Dodge, Jiayu Zhou, and Fei Wang. 2019. MetaPred: Meta-learning for clinical risk prediction with limited patient electronic health records. In 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2487–2495.

Digital Library

[150]

Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Ustebay. 2019. Bayesian graph convolutional neural networks for semi-supervised classification. In AAAI Conference on Artificial Intelligence, Vol. 33. 5829–5836.

Digital Library

[151]

Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. In International Conference on Advances in Neural Information Processing Systems. 8778–8788.

[152]

Zizhao Zhang, Yuanpu Xie, Fuyong Xing, Mason McGough, and Lin Yang. 2017. MDNet: A semantically and visually interpretable medical image diagnosis network. In IEEE Conference on Computer Vision and Pattern Recognition. 6428–6436.

[153]

Zilong Zhao, Sophie Cerf, Robert Birke, Bogdan Robu, Sara Bouchenak, Sonia Ben Mokhtar, and Lydia Y. Chen. 2019. Robust anomaly detection on unreliable data. In 49th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’19). IEEE, 630–637.

[154]

Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In IEEE Conference on Computer Vision and Pattern Recognition. 2921–2929.

[155]

Tingting Zhou, Wei Liu, Congyu Zhou, and Leiting Chen. 2018. GAN-based semi-supervised for imbalanced data classification. In 4th International Conference on Information Management (ICIM’18). IEEE, 17–21.

[156]

Peifei Zhu and Masahiro Ogino. 2019. Guideline-based additive explanation for computer-aided diagnosis of lung nodules. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. Springer, 39–47.

[157]

David Zimmerer, Simon A. A. Kohl, Jens Petersen, Fabian Isensee, and Klaus H. Maier-Hein. 2018. Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018).

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Index Terms

Deep Learning for Medical Anomaly Detection – A Survey
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Knowledge representation and reasoning
  2. Machine learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 7

September 2022

778 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3476825

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University of Sydney, Australia

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Publication History

Published: 18 July 2021

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in CSUR Volume 54, Issue 7

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