Abstract
The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data. The inadequacy in richly representative healthy and abnormal conditions results in isolated analyses of pathologies, that can not handle realistic multi-pathology scenarios. In this work, we explore how to learn more for free, from limited data through solving a WCE multicentric, multi-pathology classification problem. Learning more implies to learning more than full supervision would allow with the same data. This is done by combining self supervision with full supervision, under multi task learning. Additionally, we draw inspiration from the Human Visual System (HVS) in designing self supervision tasks and investigate if seemingly ineffectual signals within the data itself can be exploited to gain performance, if so, which signals would be better than others. Further, we present our analysis of the high level features as a stepping stone towards more robust multi-pathology CADx in WCE. Code accompanying this work will be made available on github.
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References
Ali, S., et al.: Endoscopy artifact detection (EAD 2019) challenge dataset. arXiv preprint arXiv:1905.03209 (2019)
Atsawarungruangkit, A., Elfanagely, Y., Asombang, A.W., Rupawala, A., Rich, H.G.: Understanding deep learning in capsule endoscopy: can artificial intelligence enhance clinical practice? Artif. Intell. Gastrointest. Endosc. 1(2), 33–43 (2020)
Baxter, J.: A bayesian/information theoretic model of learning to learn via multiple task sampling. Mach. Learn. 28(1), 7–39 (1997)
Benton, A., Mitchell, M., Hovy, D.: Multitask learning for mental health conditions with limited social media data. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 1, Long Papers, pp. 152–162 (2017)
Bingel, J., Søgaard, A.: Identifying beneficial task relations for multi-task learning in deep neural networks. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 2, Short Papers, pp. 164–169. Association for Computational Linguistics, Valencia, Spain, April 2017. https://www.aclweb.org/anthology/E17-2026
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997). https://doi.org/10.1023/A:1007379606734
Caruana, R.: Multitask learning: a knowledge-based source of inductive bias. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 41–48. Morgan Kaufmann (1993)
Ding, Z., et al.: Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 157(4), 1044–1054 (2019)
Hwang, Y., Park, J., Lim, Y.J., Chun, H.J.: Application of artificial intelligence in capsule endoscopy: where are we now? Clin. Endosc. 51(6), 547–551 (2018)
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)
Kokkinos, I.: Ubernet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6129–6138 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Laiz, P., Vitria, J., Seguí, S.: Using the triplet loss for domain adaptation in WCE. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 399–405 (2019)
Leenhardt, R., Li, C., Le Mouel, J.P., Rahmi, G., Saurin, J.C., Cholet, F., Boureille, A., Amiot, X., Delvaux, M., Duburque, C., et al.: CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy. Endosc. Int. Open 8(3), E415 (2020)
Liu, X., Gao, J., He, X., Deng, L., Duh, K., Wang, Y.y.: Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 912–921. Association for Computational Linguistics, Denver, Colorado, May–June 2015. https://doi.org/10.3115/v1/N15-1092, https://www.aclweb.org/anthology/N15-1092
McAlindon, M.E., Ching, H.L., Yung, D., Sidhu, R., Koulaouzidis, A.: Capsule endoscopy of the small bowel. Ann. Transl. Med. 4(19), 369 (2016)
McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3994–4003 (2016)
Mohammed, A., Farup, I., Pedersen, M., Hovde, Ø., Yildirim Yayilgan, S.: Stochastic capsule endoscopy image enhancement. J. Imaging 4(6), 75 (2018)
Muhammad, K., Khan, S., Kumar, N., Del Ser, J., Mirjalili, S.: Vision-based personalized wireless capsule endoscopy for smart healthcare: taxonomy, literature review, opportunities and challenges. Future Gener. Comput. Syst. 113, 266–280 (2020)
Park, J., Cho, Y.K., Kim, J.H.: Current and future use of esophageal capsule endoscopy. Clin. Endosc. 51(4), 317–322 (2018)
Seguí, S., Drozdzal, M., Pascual, G., Radeva, P., Malagelada, C., Azpiroz, F., Vitrià, J.: Generic feature learning for wireless capsule endoscopy analysis. Comput. Biol. Med. 79, 163–172 (2016)
Soffer, S., Klang, E., Shimon, O., Nachmias, N., Eliakim, R., Ben-Horin, S., Kopylov, U., Barash, Y.: Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest. Endosc. 92(4), 831–839 (2020)
Syed, S., Stidham, R.W.: Potential for standardization and automation for pathology and endoscopy in inflammatory bowel disease. Inflamm. Bowel Dis. 26(10), 1490–1497 (2020)
Valério, M.T., Gomes, S., Salgado, M., Oliveira, H.P., Cunha, A.: Lesions multiclass classification in endoscopic capsule frames. Procedia Comput. Sci. 164, 637–645 (2019)
Yang, Y.J.: The future of capsule endoscopy: the role of artificial intelligence and other technical advancements. Clin. Endosc. 53(4), 387–394 (2020)
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 94–108. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_7
Zheng, Y., Hawkins, L., Wolff, J., Goloubeva, O., Goldberg, E.: Detection of lesions during capsule endoscopy: physician performance is disappointing. Am. J. Gastroenterol. 107(4), 554–560 (2012)
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Vats, A., Pedersen, M., Mohammed, A., Hovde, Ø. (2021). Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_1
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