0000000000142957
AUTHOR
Miguel Goncalves
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
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…
Interrater reliability of ultrasound in the diagnosis of sialolithiasis
The aim of this study was to assess the interrater reliability of ultrasound for diagnosing sialolithiasis. A total of 100 consecutive patients with signs of obstructive sialadenopathy were evaluated. The patients all underwent ultrasound examinations in a standardized manner conducted by one specialist with extensive experience in the management of salivary gland disorders and proficiency in head and neck ultrasonography. The video recordings were sent to six colleagues with comparable experience without providing any further information about the patients' medical history and physical examination. The overall agreement between the seven observers was substantial, with a κ of 0.765 for th…
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
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 (f…