Abstract
Early pre-cancerous malignant condition such as Barrett’s esophagus (BE) and quantification of associated dysplastic changes is critical for early diagnosis and treatment. While endoscopic videos are corrupted with multiple artefacts and procedure require investigating extended areas such as stomach, it is inevitable that there is risk of missing areas that may potentially harbour neoplastic changes and require immediate attention. A complete guidance assisting navigation is thus vital. Visually obvious neoplasia and suspected areas both needs be flagged for biopsies. Due to the thin demarcation between BE and early neoplasia, it is often challenging to identify subtle changes even by experts. We propose a self-supervised learning technique for a fully assistive esophageal endoscopy surveillance system. The self-supervision step allows to learn complex representations using a pretext task of solving a jigsaw puzzle. Here, the idea is to enable network to distinctly learn inconspicuous features that are characteristics of neoplasia and other classes. In order to enable an optimal decision boundary we propose to incorporated angular margin in our fine-tuning process. Our proposed framework showed a boost of 3% on overall accuracy compared to fully supervised approach with similar backbone.
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References
Ali, S., et al.: A deep learning framework for quality assessment and restoration in video endoscopy. Med. Image Anal. 68, 101900 (2021)
Azizi, S., et al.: Big self-supervised models advance medical image classification. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3458–3468 (2021)
Chang, Y.Y., et al.: Development and validation of a deep learning-based algorithm for colonoscopy quality assessment. Surg. Endosc. (2022)
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 8765–8775 (2020)
de Groof, A.J., et al.: Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology 158(4), 915-929.e4 (2020)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Ebigbo, A., et al.: Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 68(7), 1143–1145 (2019)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, Q., et al.: Deep learning-based anatomical site classification for upper gastrointestinal endoscopy. Int. J. Comput. Assist. Radiol. Surg. 15(7), 1085–1094 (2020). https://doi.org/10.1007/s11548-020-02148-5
Hussein, M., et al.: Deep neural network for the detection of early neoplasia in Barrett’s oesophagus. Gastrointest. Endosc. 91(6), AB250 (2020)
Jung-Whan, P., Yoon, K., Woo-Jin, K., Seung-Joo, N.: Automatic anatomical classification model of esophagogastroduodenoscopy images using deep convolutional neural networks for guiding endoscopic photodocumentation. J. Korea Soc. Comput. Inf. 26(3), 19–28 (2021)
Liu, G., et al.: Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann. Transl. Med. 8(7) (2020)
Misra, I., Maaten, L.V.D.: Self-supervised learning of pretext-invariant representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6707–6717 (2020)
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervised learning for few-shot medical image segmentation. IEEE Trans. Med. Imaging (2022). https://doi.org/10.1109/TMI.2022.3150682
Takiyama, H., et al.: Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci. Rep. 8(1), 1–8 (2018)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Wu, Z., Ge, R., Wen, M., Liu, G., et al.: ELNet: automatic classification and segmentation for esophageal lesions using convolutional neural network. Med. Image Anal. 67, 101838 (2021)
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Xu, Z., Ali, S., Celik, N., Bailey, A., Braden, B., Rittscher, J. (2022). Self-supervised Approach for a Fully Assistive Esophageal Surveillance: Quality, Anatomy and Neoplasia Guidance. In: Ali, S., van der Sommen, F., Papież, B.W., van Eijnatten, M., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention Through Early Detection. CaPTion 2022. Lecture Notes in Computer Science, vol 13581. Springer, Cham. https://doi.org/10.1007/978-3-031-17979-2_2
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