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Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections.
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.
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity.
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
We find that the network trained on the oral cavity data reaches an accuracy of 89. 45% and an area-under-the-curve (AUC) value of 0. 955, when applied on the vocal cords data.
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion.
