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Squamous Cell Carcinoma (SCC) is the most common cancer type of theepithelium and is often detected at a late stage. Besides invasive diagnosis ofSCC by means of biopsy and histo-pathologic assessment, Confocal LaserEndomicroscopy (CLE) has emerged as noninvasive method that was successfullyused to diagnose SCC in vivo. For interpretation of CLE images, however,extensive training is required, which limits its applicability and use inclinical practice of the method. To aid diagnosis of SCC in a broader scope,automatic detection methods have been proposed. This work compares two methodswith regard to their applicability in a transfer learning sense, i.e. trainingon one tissue type (from one clinical team) and applying the learntclassification system to another entity (different anatomy, different clinicalteam). Besides a previously proposed, patch-based method based on convolutionalneural networks, a novel classification method on image level (based on apre-trained Inception V.3 network with dedicated preprocessing andinterpretation of class activation maps) is proposed and evaluated. The newlypresented approach improves recognition performance, yielding accuracies of91.63% on the first data set (oral cavity) and 92.63% on a joint data set. Thegeneralization from oral cavity to the second data set (vocal folds) lead tosimilar area-under-the-ROC curve values than a direct training on the vocalfolds data set, indicating good generalization.

communications in computer and information science

Transferability of Deep Learning Algorithms for Malignancy Detection in  Confocal Laser Endomicroscopy Images from Different Anatomical Locations of  the Upper Gastrointestinal Tract