Abstract
A large number of different lesions and pathologies can affect the human digestive system, resulting in life-threatening situations. Early detection plays a relevant role in the successful treatment and the increase of current survival rates to, e.g., colorectal cancer. The standard procedure enabling detection, endoscopic video analysis, generates large quantities of visual data that need to be carefully analyzed by an specialist. Due to the wide range of color, shape, and general visual appearance of pathologies, as well as highly varying image quality, such process is greatly dependent on the human operator experience and skill. In this work, we detail our solution to the task of multi-category classification of images from the gastrointestinal (GI) human tract within the 2020 Endotect Challenge. Our approach is based on a Convolutional Neural Network minimizing a hierarchical error function that takes into account not only the finding category, but also its location within the GI tract (lower/upper tract), and the type of finding (pathological finding/therapeutic intervention/anatomical landmark/mucosal views’ quality). We also describe in this paper our solution for the challenge task of polyp segmentation in colonoscopies, which was addressed with a pretrained double encoder-decoder network. Our internal cross-validation results show an average performance of 91.25 Mathews Correlation Coefficient (MCC) and 91.82 Micro-F1 score for the classification task, and a 92.30 F1 score for the polyp segmentation task. The organization provided feedback on the performance in a hidden test set for both tasks, which resulted in 85.61 MCC and 86.96 F1 score for classification, and 91.97 F1 score for polyp segmentation. At the time of writing no public ranking for this challenge had been released.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahn, S.B., Han, D.S., Bae, J.H., Byun, T.J., Kim, J.P., Eun, C.S.: The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies. Gut Liver 6(1), 64–70 (2012)
Bernal, J., et al.: Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge. IEEE Trans. Med. Imaging 36(6), 1231–1249 (2017)
Borgli, H., et al.: HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci. Data 7(1), 283 (2020)
Carneiro, G., Pu, Z.C.T.L., Singh, R., Burt, A.: Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Med. Image Anal. 62, 101653 (2020)
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 4470–4478. Curran Associates Inc., Red Hook, December 2017
Galdran, A., Anjos, A., Dolz, J., Chakor, H., Lombaert, H., Ayed, I.B.: The little w-net that could: state-of-the-art retinal vessel segmentation with minimalistic models. arXiv:2009.01907, September 2020
Galdran, A., González Ballester, M.A., Carneiro, G.: Double encoder-decoder networks for gastrointestinal polyp segmentation. In: ICPR Workshop on Artificial Intelligence for Healthcare Applications (2020)
Haggar, F.A., Boushey, R.P.: Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin. Colon Rectal Surg. 22(4), 191–197 (2009)
Hicks, S., Jha, D., Thambawita, V., Halvorsen, P., Hammer, H.L., Riegler, M.: The EndoTect 2020 challenge: evaluation and comparison of classification, segmentation and inference time for endoscopy. In: ICPR (2020)
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37
Kolesnikov, A., et al.: Big Transfer (BiT): general visual representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 491–507. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58558-7_29
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944, July 2017. ISSN 1063–6919
Lui, T.K., et al.: New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest. Endosc. 93, 193–200.e1 (2020)
Sánchez-Peralta, L.F., Bote-Curiel, L., Picón, A., Sánchez-Margallo, F.M., Pagador, J.B.: Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artif. Intell. Med. 108, 101923 (2020)
Vázquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthcare Eng. 2017, 4037190 (2017)
Wickstrøm, K., Kampffmeyer, M., Jenssen, R.: Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Med. Image Anal. 60, 101619 (2020)
Zhang, R., Zheng, Y., Poon, C.C.Y., Shen, D., Lau, J.Y.W.: Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recogn. 83, 209–219 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Galdran, A., Carneiro, G., Ballester, M.A.G. (2021). A Hierarchical Multi-task Approach to Gastrointestinal Image Analysis. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-68793-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68792-2
Online ISBN: 978-3-030-68793-9
eBook Packages: Computer ScienceComputer Science (R0)