Abstract
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated.
The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aubreville, M., et al.: Patch-based carcinoma detection on confocal laser endomicroscopy images - a cross-site robustness assessment. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOIMAGING, vol. 2, pp. 27–34. INSTICC, SciTePress (2018). https://doi.org/10.5220/0006534700270034
Aubreville, M., et al.: Automatic classification of cancerous tissue in laser endomicroscopy images of the oral cavity using deep learning. Sci. Rep. 7(1), s41598–017 (2017). https://doi.org/10.1038/s41598-017-12320-8
Betz, C.S., et al.: Optical diagnostic methods for early tumour diagnosis in the upper aerodigestive tract. HNO 64(1), 41–48 (2016). https://doi.org/10.1007/s00106-015-0104-8
Chauhan, S.S., et al.: Confocal laser endomicroscopy. Gastrointest. Endosc. 80(6), 928–938 (2014). https://doi.org/10.1016/j.gie.2014.06.021
Cikojević, D., Glunčić, I., Pešutić-Pisac, V.: Comparison of contact endoscopy and frozen section histopathology in the intra-operative diagnosis of laryngeal pathology. J. Laryngol. Otol. 122(8), 836–839 (2008). https://doi.org/10.1017/S0022215107000539
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056
Forastiere, A., Koch, W., Trotti, A., Sidransky, D.: Head and neck cancer. N. Engl. J. Med. 345(26), 1890–1900 (2001). https://doi.org/10.1056/NEJMra001375
Goncalves, M., Iro, H., Dittberner, A., Agaimy, A., Bohr, C.: Value of confocal laser endomicroscopy in the diagnosis of vocal cord lesions. Eur. Rev. Med. Pharmacol. Sci. 21, 3990–3997 (2017)
Goncalves, M., et al.: Probe-based confocal laser endomicroscopy in detecting malignant lesions of the vocal folds. Acta Otorhinolaryngol. Ital. (2019). https://doi.org/10.14639/0392-100X-2121
Izadyyazdanabadi, M., et al.: Weakly-supervised learning-based feature localization for confocal laser endomicroscopy glioma images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 300–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_34
Izadyyazdanabadi, M., et al.: Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks. In: Proceedings of the SPIE, vol. 10134, p. 101342J (2017). https://doi.org/10.1117/12.2254902
Jaremenko, C., et al.: Classification of confocal laser endomicroscopic images of the oral cavity to distinguish pathological from healthy tissue. In: Handels, H., Deserno, T.M., Meinzer, H.-P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2015. INFORMAT, pp. 479–485. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46224-9_82
Knipfer, C., et al.: Raman difference spectroscopy: a non-invasive method for identification of oral squamous cell carcinoma. Biomed. Opt. Express 5(9), 3252–3265 (2014). https://doi.org/10.1364/BOE.5.003252
Lüllmann-Rauch, R., Paulsen, F.: Taschenlehrbuch Histologie, 4th edn. Thieme, Stuttgart (2012)
Maier, H., Dietz, A., Gewelke, U., Heller, W., Weidauer, H.: Tobacco andalcohol and the risk of head and neck cancer. Clin. Investig. 70(3–4), 320–327 (1992). https://doi.org/10.1007/BF00184668
Murthy, V.N., Singh, V., Sun, S., Bhattacharya, S., Chen, T., Comaniciu, D.: Cascaded deep decision networks for classification of endoscopic images. In: Proceedings of the SPIE, vol. 10133 (2017). https://doi.org/10.1117/12.2254333
Muto, M.: Squamous cell carcinoma in situ at oropharyngeal and hypopharyngeal mucosal sites. Cancer 101(6), 1375–1381 (2004). https://doi.org/10.1002/cncr.20482
Neumann, H., Kiesslich, R., Wallace, M.B., Neurath, M.F.: Confocal laser endomicroscopy: technical advances and clinical applications. Gastroenterology 139(2), 388–392.e2 (2010). https://doi.org/10.1053/j.gastro.2010.06.029
Neumann, H., Langner, C., Neurath, M.F., Vieth, M.: Confocal laser endomicroscopy for diagnosis of barrett’s esophagus. Front. Oncol. 2 (2012). https://doi.org/10.3389/fonc.2012.00042
Neumann, H., Vieth, M., Atreya, R., Neurath, M.F., Mudter, J.: Prospective evaluation of the learning curve of confocal laser endomicroscopy in patients with IBD. Histol. Histopathol. 26(7), 867–872 (2011). https://doi.org/10.14670/HH-26.867
Oetter, N., et al.: Development and validation of a classification and scoring system for the diagnosis of oral squamous cell carcinomas through confocal laser endomicroscopy. J. Transl. Med. 14(1), 1–11 (2016). https://doi.org/10.1186/s12967-016-0919-4
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free? - weakly-supervised learning with convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 685–694. IEEE (2015). https://doi.org/10.1109/CVPR.2015.7298668
Parkin, D.M., Bray, F., Ferlay, J., Pisani, P.: Global cancer statistics, 2002. CA: Cancer J. Clin. 55(2), 74–108 (2005). https://doi.org/10.3322/canjclin.55.2.74
Robert Koch Institut: Zentrum für Krebsregisterdaten: Krebs in Deutschland für 2013/2014, 11th edn. Robert Koch Institut, Berlin (2017)
Rohen, J.W.: Histologische Differentialdiagnose, 5th edn. Schattauer, Stuttgart (1994)
Rohen, J.W., Lütjen-Drecoll, E.: Funktionelle Histologie, 4th edn. Schattauer, Stuttgart (2000)
Stoeve, M., et al.: Motion artifact detection in confocal laser endomicroscopy images. Bildverarbeitung für die Medizin 2018. INFORMAT, pp. 328–333. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56537-7_85
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), September 2015. https://doi.org/10.1109/CVPR.2015.7298594
Vo, K., Jaremenko, C., Bohr, C., Neumann, H., Maier, A.: Automatic classification and pathological staging of confocal laser endomicroscopic images of the vocal cords. Bildverarbeitung für die Medizin 2017. INFORMAT, pp. 312–317. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54345-0_70
Westra, W.H.: The pathology of HPV-related head and neck cancer: implications for the diagnostic pathologist. Semin. Diagn. Pathol. 32(1), 42–53 (2015). https://doi.org/10.1053/j.semdp.2015.02.023
Xing, F., Xie, Y., Su, H., Liu, F., Yang, L.: Deep learning in microscopy image analysis: a survey. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–19 (2017). https://doi.org/10.1109/TNNLS.2017.2766168
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Aubreville, M. et al. (2019). Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-29196-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29195-2
Online ISBN: 978-3-030-29196-9
eBook Packages: Computer ScienceComputer Science (R0)