Abstract: The present invention relates to a computer-implemented method capable of automatically classifying and differentiating biliary lesions in images obtained from a digital cholangioscopy system, characterizing them according to their malignant potential, through the classification of pixels as a malignant lesion, or benign lesion, followed by a characterization stage and indexing of such lesions according to a set of morphologic characteristics with clinical relevance, namely the presence/absence of tumor vessels, the presence/absence of papillary projections, the presence/absence of intraductal nodules and the presence/absence of tumor masses.

Abstract: The present invention relates to a computer-implemented method capable of automatically detecting small bowel and colonic ulcers and erosions in Crohn's capsule endoscopy image data, by classifying pixels as lesion or non-lesion, using a convolutional image feature extraction step followed by a classification step and indexing such lesions in the set of one or more classes.

Abstract: The present invention relates to a computer-implemented method capable to automatically characterize small bowel lesions in capsule endoscopy images, comprising detecting small bowel lesions in medical images by classifying pixels as lesion or non-lesion, using a convolutional image feature extraction step followed by an architecture of classification and indexing into one or more classes.

Abstract: The present invention relates to a computer-implemented method, capable of automatically detecting clinically relevant colonic pleomorphic lesions and blood or hematic traces in colon capsule endoscopy images, by classifying pixels as colonic pleomorphic lesions or blood or hematic traces, using a convolutional image feature extraction step followed by a classification and indexing step of such findings into a set of one or more classes.