TY - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas ED - Wiebe, Sheldon ED - Gamboa, Hugo ED - Fred, Ana ED - Bermúdez i Badia, Sergi T1 - Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images BT - A Cross-Site Robustness Assessment T2 - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies N2 - Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accurac y of 89.45% and an area-under-the-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner. UR - https://doi.org/10.5220/0006534700270034 KW - Automatic Carcinoma Detection KW - Confocal Laser Endomicroscopy KW - Deep Convolutional Networks KW - Squamous Cell Carcinoma Y1 - 2018 UR - https://doi.org/10.5220/0006534700270034 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-26277 SN - 978-989-758-278-3 SP - 27 EP - 34 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas T1 - Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract T2 - Biomedical Engineering Systems and Technologies UR - https://doi.org/10.1007/978-3-030-29196-9_4 KW - Confocal Laser Endomicroscopy KW - Transfer learning KW - Head and neck squamous cell carcinoma Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-29196-9_4 SN - 978-3-030-29195-2 SN - 978-3-030-29196-9 SN - 1865-0929 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 67 EP - 85 PB - Springer CY - Cham ER -