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大学・研究所にある論文を検索できる 「アノテーションが限定された生物・医学データのための分類手法の提案」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

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アノテーションが限定された生物・医学データのための分類手法の提案

原田, 翔太 HARADA, Shota ハラダ, ショウタ 九州大学

2022.03.23

概要

This thesis aims to solve the problem of limited annotation in several bio-medical data analysis tasks. Specifically, data augmentation, group-based labeling utilizing constrained clustering, and semi-supervised learning are proposed as the approaches. First, as data augmentation, I proposed a time-series generation method based on Generative Adversarial Networks and applied it to a biosignal classification task. It generates various time-series from data with limited annotations and contributes to provide more training samples for a classifier. Second, I proposed a new constrained clustering method, where a user attaches annotations to several sample pairs. Annotations are two types: cannot-link and must-link. The pair with cannot-link should not belong to the same cluster, whereas the pair with must-link should belong. These annotations are useful especially for medical data, because medical experts can have a more expected clustering result by a small number of annotations. Moreover, those annotations are treated as soft-constraints and therefore medical experts can attach them without extreme carefulness. Finally, I proposed order-guided disentangled representation learning, which is semi-supervised learning for bio-medical data classification. This method performs disentangled representation learning with prior knowledge that is effective for learning bio-medical data classification tasks. This method could improve classification performance even with limited annotation by effectively utilizing the prior knowledge through disentangled representation learning.

参考文献

[1] L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579–2605, 2008. [Online]. Available: http://jmlr.org/papers/v9/vandermaaten08a.html

[2] H. Le, A. Eriksson, T.-T. Do, and M. Milford, “A binary optimization approach for constrained K-means clustering,” in Proceeding of the Asian Conference on Computer Vision, September 2018, pp. 577–584.

[3] S. M. W. Casimir A. Kulikowski, “Representation of expert knowledge for con- sultation: The casnet and expert projects,” Artificial Intelligence in Medicine, pp. 21–55, Jan 2019.

[4] I. Hatzilygeroudis and J. Prentzas, “Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems,” Expert Systems with Applications, vol. 27, no. 1, pp. 63–75, 2004. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417403002100

[5] Y. Shen, J. Colloc, A. Jacquet-Andrieu, and K. Lei, “Emerging medical informatics with case-based reasoning for aiding clinical decision in multi- agent system,” Journal of Biomedical Informatics, vol. 56, pp. 307–317, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1532046415001227

[6] G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Scientific Reports, vol. 6, no. 1, p. 26286, May 2016. [Online]. Available: https://doi.org/10.1038/srep26286

[7] M. Bakator and D. Radosav, “Deep learning and medical diagnosis: A review of literature,” Multimodal Technologies and Interaction, vol. 2, no. 3, 2018. [Online]. Available: https://www.mdpi.com/2414-4088/2/3/47

[8] W. Sun, B. Zheng, and W. Qian, “Computer aided lung cancer diagnosis with deep learning algorithms,” in Medical imaging 2016: computer-aided diagnosis, vol. 9785. International Society for Optics and Photonics, 2016, p. 97850Z.

[9] T. M. K. Connor Shorten, “A survey on image data augmentation for deep learn- ing,” Journal of Big Data, 2019.

[10] B. K. Iwana and S. Uchida, “An empirical survey of data augmentation for time series classification with neural networks,” PLOS ONE, vol. 16, no. 7, pp. 1–32, 07 2021. [Online]. Available: https://doi.org/10.1371/journal.pone.0254841

[11] M. Wigness, B. A. Draper, and R. J. Beveridge, “Efficient label collection for unlabeled image datasets,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 108, May 2015, pp. 4594–4602.

[12] M. Wigness, B. A. Draper, and J. R. Beveridge, “Efficient label collection for image datasets via hierarchical clustering,” International Journal of Computer Vision, vol. 126, pp. 59–85, January 2018.

[13] E. Arazo, D. Ortego, P. Albert, N. E. O’Connor, and K. McGuinness, “Pseudo- labeling and confirmation bias in deep semi-supervised learning,” in Proceedings of the International Joint Conference on Neural Networks, 2020.

[14] A. Biswas and D. W. Jacobs, “Active image clustering: Seeking constraints from humans to complement algorithms,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2152–2159.

[15] C. Galleguillos, B. McFee, and G. R. G. Lanckriet, “Iterative category discovery via multiple kernel metric learning,” International Journal of Computer Vision, vol. 108, pp. 115–132, May 2014.

[16] S. Mousavi, D. Lee, T. Griffin, D. Steadman, and A. Mockus, “Collaborative learning of semi-supervised clustering and classification for labeling uncurated data,” 2020.

[17] Y. J. Lee and K. Grauman, “Object-graphs for context-aware visual category discovery,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 346–358, 2012.

[18] T. Tuytelaars, C. H. Lampert, M. B. Blaschko, and W. Buntine, “Unsupervised object discovery: A comparison,” International Journal of Computer Vision, vol. 88, no. 2, pp. 284–302, 2010.

[19] D. Dai, M. Prasad, C. Leistner, and L. Van Gool, “Ensemble partitioning for unsupervised image categorization,” in Proceeding of the European Conference on Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 483–496.

[20] C. Xiong, D. Johnson, and J. Corso, “Spectral active clustering via purification of the 𝑘-nearest neighbor graph,” in Proceeding of the European Conference on Data Mining, January 2012.

[21] D.-H. Lee, “Pseudo-Label : The simple and efficient semi-supervised learning method for deep neural networks,” in Proceedings of the ICML 2013 Workshop: Challenges in Representation Learning, 2013.

[22] K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “FixMatch: Simplifying semi-supervised learning with consistency and confidence,” in Proceedings of the Advances in Neural Informa- tion Processing Systems, vol. 33, 2020, pp. 596–608.

[23] K. Cao, J. Ji, Z. Cao, C.-Y. Chang, and J. C. Niebles, “Few-shot video classifi- cation via temporal alignment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.

[24] D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, “Tempo- ral cycle-consistency learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.

[25] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014, pp. 2672–2680.

[26] A. H. Liu, Y.-C. Liu, Y.-Y. Yeh, and Y.-C. F. Wang, “A unified feature disen- tangler for multi-domain image translation and manipulation,” in Proceedings of the Advances in Neural Information Processing Systems, vol. 31, 2018.

[27] Y. Liu, F. Wei, J. Shao, L. Sheng, J. Yan, and X. Wang, “Exploring disen- tangled feature representation beyond face identification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

[28] C. Bass, M. da Silva, C. Sudre, P.-D. Tudosiu, S. M. Smith, and E. C. Robinson, “ICAM: Interpretable classification via disentangled representations and feature attribution mapping,” in Proceedings of International Conference on Neural In- formation Processing Systems, 2020.

[29] Z. Zhang, L. Tran, X. Yin, Y. Atoum, X. Liu, J. Wan, and N. Wang, “Gait recog- nition via disentangled representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4710–4719.

[30] R. Cai, Z. Li, P. Wei, J. Qiao, K. Zhang, and Z. Hao, “Learning disentangled semantic representation for domain adaptation,” in Proceedings of the Interna- tional Conference on Artificial Intelligence, vol. 2019. NIH Public Access, 2019, p. 2060.

[31] V.-H. Tran and C.-C. Huang, “Domain adaptation meets disentangled represen- tation learning and style transfer,” in Proceedings of IEEE International Confer- ence on Systems, Man and Cybernetics. IEEE, 2019, pp. 2998–3005.

[32] L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele, and M. Fritz, “Disen- tangled person image generation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 99–108.

[33] P. Kora, “ECG based myocardial infarction detection using hybrid firefly algorithm,” Comp. Methods Programs Biomedicine, vol. 152, pp. 141 – 148, 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0169260717303516

[34] L. Wang, W. Xue, Y. Li, M. Luo, J. Huang, W. Cui, and C. Huang, “Automatic epileptic seizure detection in eeg signals using multi-domain feature extraction and nonlinear analysis,” Entropy, vol. 19, no. 6, 2017. [Online]. Available: http://www.mdpi.com/1099-4300/19/6/222

[35] A. Khodayari-Rostamabad, J. P. Reilly, G. Hasey, H. deBruin, and D. Mac- Crimmon, “Diagnosis of psychiatric disorders using eeg data and employing a statistical decision model,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Aug 2010, pp. 4006–4009.

[36] S. Sudarsan and E. C. Sekaran, “Design and development of emg controlled prosthetics limb,” Procedia Engineering, vol. 38, pp. 3547 – 3551, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S1877705812023223

[37] Y. Rahul, M. Rupam, and R. Sharma, “A review on EEG control smart wheel chair,” International Journal of Advanced Research in Computer Science, vol. 8, pp. 501–507, 12 2017.

[38] K. LaFleur, K. Cassady, A. Doud, K. Shades, E. Rogin, and B. He, “Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface,” J. Neural Eng., vol. 10, no. 4, p. 046003, 2013. [Online]. Available: http://stacks.iop.org/1741-2552/10/i=4/a=046003

[39] B. Pourbabaee, M. J. Roshtkhari, and K. Khorasani, “Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, pp. 1–10, June 2017.

[40] S. Chambon, M. N. Galtier, P. J. Arnal, G. Wainrib, and A. Gramfort, “A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series,” IEEE Transactions on Neural Systems and Reha- bilitation Engineering, vol. 26, no. 4, pp. 758–769, Apr. 2018.

[41] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp. 105–114.

[42] T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in International Conference on Learning Representations, 2018.

[43] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image trans- lation using cycle-consistent adversarial networks,” in IEEE International Con- ference Computer Vision, October 2017.

[44] O. Mogren, “C-RNN-GAN: A continuous recurrent neural network with adver- sarial training,” in Constructive Mach. Learning Workshop NIPS, Dec. 2016.

[45] L. Yu, W. Zhang, J. Wang, and Y. Yu, “SeqGAN: Sequence generative adversar- ial nets with policy gradient,” in Association for the Advencement of Artificial Intelligenece, Aug. 2017.

[46] Y.-H. Y. Hao-Wen Dong, “Convolutional generative adversarial networks with bi- nary neurons for polyphonic music generation,” in The 19th International Society for Music Information Retrieval Conference, Paris, France, 2018, pp. 190–196.

[47] C. Esteban, S. L. Hyland, and G. Rätsch, “Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs,” arXiv e-prints, p. arXiv:1706.02633, Jun 2017.

[48] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Compu- tation, vol. 9, no. 8, pp. 1735–1780, 1997.

[49] A. Koski, “Modelling ecg signals with hidden markov models,” Artificial Intell. Medicine, vol. 8, no. 5, pp. 453 – 471, 1996. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0933365796003521

[50] T. Yamanobe, K. Pakdaman, T. Nomura, and S. Sato, “Analysis of the response of a pacemaker neuron model to transient inputs,” Biosystems, vol. 48, no. 1, pp. 287 – 295, 1998. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0303264798000768

[51] D. Farina, A. Crosetti, and R. Merletti, “A model for the generation of synthetic intramuscular emg signals to test decomposition algorithms,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 1, pp. 66–77, Jan 2001.

[52] F. Wendling, J. J. Bellanger, F. Bartolomei, and P. Chauvel, “Relevance of nonlinear lumped-parameter models in the analysis of depth-eeg epileptic signals,” Biological Cybernetics, vol. 83, no. 4, pp. 367–378, Sept. 2000. [Online]. Available: https://doi.org/10.1007/s004220000160

[53] P. E. McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, “A dynamical model for generating synthetic electrocardiogram signals,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 289–294, March 2003.

[54] M. J. Rempe, J. Grønli, T. T. Pedersen, J. Mrdalj, A. Marti, P. Meerlo, and J. P. Wisor, “Mathematical modeling of sleep state dynamics in a rodent model of shift work,” Neurobiology of Sleep and Circadian Rhythms, vol. 5, pp. 37 – 51, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S2451994417300111

[55] F. L. Da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski, and D. N. Velis, “Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity,” Epilepsia, vol. 44, no. s12, pp. 72–83, 2003. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0013-9580.2003.12005.x

[56] A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier GANs,” in Proceedings of the 34th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. International Convention Centre, Sydney, Australia: PMLR, Aug. 2017, pp. 2642–2651. [Online]. Available: http://proceedings.mlr.press/v70/odena17a.html

[57] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “In- foGAN: Interpretable representation learning by information maximizing gener- ative adversarial nets,” in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 2172–2180.

[58] L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein, “Unrolled generative adver- sarial networks,” in Int. Conf. Learning Representations, 2017.

[59] A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, “The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data Mining and Knowledge Discovery, vol. Online First, 2016.

[60] R. T. Olszewski, “Generalized feature extraction for structural pattern recogni- tion in time-series data,” Ph.D. dissertation, Carnegie Mellon University, Pitts- burgh, PA, USA, 2001, aAI3040489.

[61] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Physical Rev. E, vol. 64, pp. 061 907–1–061 907–8, Nov. 2001. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevE.64.061907

[62] Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu, “Advances in optimizing recurrent networks,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, pp. 8624–8628.

[63] Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, “The UCR time series classification archive,” July 2015, www.cs.ucr.edu/~eamonn/time_series_data/.

[64] R. Bellman and R. Kalaba, “On adaptive control processes,” IRE Transactions on Automatic Control, vol. 4, no. 2, pp. 1–9, November 1959.

[65] H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spo- ken word recognition,” IEEE Trans. Acoust, Speech, Signal Process, vol. 26, no. 1, pp. 43–49, Feb. 1978.

[66] D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Inter- national Conference on Learning Representations, 2015.

[67] K. Wagstaff, C. Cardie, S. Rogers, and S. Schrödl, “Constrained K-means cluster- ing with background knowledge,” in Proceedings of the International Conference on Machine Learning, ser. ICML ’01. San Francisco, CA, USA: Morgan Kauf- mann Publishers Inc., 2001, pp. 577—-584.

[68] N. Shental, A. Bar-Hillel, T. Hertz, and D. Weinshall, “Computing gaussian mixture models with em using equivalence constraints,” in Proceedings of the In- ternational Conference on Neural Information Processing Systems, ser. NIPS’03. Cambridge, MA, USA: MIT Press, 2003, pp. 465—-472.

[69] Z. Li, J. Liu, and X. Tang, “Constrained clustering via spectral regularization,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 421–428.

[70] Z. Li and J. Liu, “Constrained clustering by spectral kernel learning,” Proceeding of IEEE International Conference on Computer Vision, pp. 421–427, 2009.

[71] I. Davidson and S. S. Ravi, “Clustering with constraints: Feasibility issues and the K-means algorithm,” in Proceedings of the SIAM international conference on data mining, April 2005, pp. 138–149.

[72] D. Pelleg and D. Baras, “K-means with large and noisy constraint sets,” in Pro- ceeding of the European Conference on Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 674–682.

[73] M. E. Ares, J. Parapar, and Á. Barreiro, “Avoiding bias in text clustering us- ing constrained K-means and may-not-links,” in Proceedings of the International Conference on the Theory of Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 322–329.

[74] S. Basu, A. Banerjee, and R. J. Mooney, “Active semi-supervision for pairwise constrained clustering,” in Proceedings of the SIAM International Conference on Data Mining, April 2004, pp. 333–344.

[75] M. Bilenko, S. Basu, and R. J. Mooney, “Integrating constraints and metric learning in semi-supervised clustering,” in Proceedings of the International Conference on Machine Learning, ser. ICML ’04. New York, NY, USA: Association for Computing Machinery, 2004, pp. 81–88. [Online]. Available: https://doi.org/10.1145/1015330.1015360

[76] Y.-C. Hsu and Z. Kira, “Neural network-based clustering using pairwise con- straints,” 2015.

[77] H. Zhang, S. Basu, and I. Davidson, “A framework for deep constrained clustering – algorithms and advances,” 2019.

[78] S. Fogel, H. Averbuch-Elor, J. Goldberger, and D. Cohen-Or, “Clustering-driven deep embedding with pairwise constraints,” 2018.

[79] D. Chakrabarti, R. Kumar, and A. Tomkins, “Evolutionary clustering,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’06. New York, NY, USA: Association for Computing Machinery, August 2006, pp. 554––560. [Online]. Available: https://doi.org/10.1145/1150402.1150467

[80] T. Mai, S. Amer-Yahia, and A. Chouakria, “Scalable active temporal constrained clustering,” in Proceedings of International Conference on Extending Database Technology. Open Proceedings, March 2018, pp. 449–452.

[81] L. Maier-Hein, S. Mersmann, D. Kondermann, C. Stock, H. G. Kenngott, A. Sanchez, M. Wagner, A. Preukschas, A.-L. Wekerle, S. Helfert, S. Boden- stedt, and S. Speidel, “Crowdsourcing for reference correspondence generation in endoscopic images,” in Medical Image Computing and Computer-Assisted Inter- vention. Cham: Springer International Publishing, 2014, pp. 349–356.

[82] T. S. Kim, A. Malpani, A. Reiter, G. D. Hager, S. Sikder, and S. Swaroop Vedula, “Crowdsourcing annotation of surgical instruments in videos of cataract surgery,” in Intravascular Imaging and Computer Assisted Stenting and Large-Scale An- notation of Biomedical Data and Expert Label Synthesis, D. Stoyanov, Z. Tay- lor, S. Balocco, R. Sznitman, A. Martel, L. Maier-Hein, L. Duong, G. Zahnd, S. Demirci, S. Albarqouni, S.-L. Lee, S. Moriconi, V. Cheplygina, D. Mateus, E. Trucco, E. Granger, and P. Jannin, Eds. Cham: Springer International Publishing, October 2018, pp. 121–130.

[83] V. Cheplygina, A. Perez-Rovira, W. Kuo, H. A. W. M. Tiddens, and M. de Brui- jne, “Early experiences with crowdsourcing airway annotations in chest ct,” in Deep Learning and Data Labeling for Medical Applications. Cham: Springer International Publishing, September 2016, pp. 209–218.

[84] R. M. Rifkin and R. A. Lippert, “Notes on regularized least-squares,” Mas- sachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, Tech. Rep., 2007.

[85] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2261–2269.

[86] J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.

[87] D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “MixMatch: A holistic approach to semi-supervised learning,” in Proceedings of the Advances in Neural Information Processing Systems, vol. 32, 2019.

[88] J. Herp, U. Deding, M. M. Buijs, R. Kroijer, G. Baatrup, and E. S. Nadimi, “Fea- ture point tracking-based localization of colon capsule endoscope,” Diagnostics, vol. 11, no. 2, 2021.

[89] K. Mori, D. Deguchi, J.-i. Hasegawa, Y. Suenaga, J.-i. Toriwaki, H. Takabatake, and H. Natori, “A method for tracking the camera motion of real endoscope by epipolar geometry analysis and virtual endoscopy system,” in Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2001, pp. 1–8.

[90] S. Harada, H. Hayashi, R. Bise, K. Tanaka, Q. Meng, and S. Uchida, “Endo- scopic image clustering with temporal ordering information based on dynamic programming,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019, pp. 3681–3684.

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