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Effective and rapid detection of lesions in the Gastrointestinal tract iscritical to gastroenterologist's response to some life-threatening diseases.Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopyprocedure by allowing gastroenterologists visualize the entire GI tractnon-invasively. Once the tiny capsule is swallowed, it sequentially captureimages of the GI tract at about 2 to 6 frames per second (fps). A single videocan last up to 8 hours producing between 30,000 to 100,000 images. Automatingthe detection of frames containing specific lesion in WCE video would relievegastroenterologists the arduous task of reviewing the entire video beforemaking diagnosis. While the WCE produces large volume of images, only about 5\%of the frames contain lesions that aid the diagnosis process. ConvolutionalNeural Network (CNN) based models have been very successful in various imageclassification tasks. However, they suffer excessive parameters, are sampleinefficient and rely on very large amount of training data. Deploying a CNNclassifier for lesion detection task will require time-to-time fine-tuning togeneralize to any unforeseen category. In this paper, we propose a metric-basedlearning framework followed by a few-shot lesion recognition in WCE data.Metric-based learning is a meta-learning framework designed to establishsimilarity or dissimilarity between concepts while few-shot learning (FSL) aimsto identify new concepts from only a small number of examples. We train afeature extractor to learn a representation for different small bowel lesionsusing metric-based learning. At the testing stage, the category of an unseensample is predicted from only a few support examples, thereby allowing themodel to generalize to a new category that has never been seen before. Wedemonstrated the efficacy of this method on real patient capsule endoscopydata.

Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video