  Effective and rapid detection of lesions in the Gastrointestinal tract is
critical to gastroenterologist's response to some life-threatening diseases.
Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy
procedure by allowing gastroenterologists visualize the entire GI tract
non-invasively. Once the tiny capsule is swallowed, it sequentially capture
images of the GI tract at about 2 to 6 frames per second (fps). A single video
can last up to 8 hours producing between 30,000 to 100,000 images. Automating
the detection of frames containing specific lesion in WCE video would relieve
gastroenterologists the arduous task of reviewing the entire video before
making diagnosis. While the WCE produces large volume of images, only about 5\%
of the frames contain lesions that aid the diagnosis process. Convolutional
Neural Network (CNN) based models have been very successful in various image
classification tasks. However, they suffer excessive parameters, are sample
inefficient and rely on very large amount of training data. Deploying a CNN
classifier for lesion detection task will require time-to-time fine-tuning to
generalize to any unforeseen category. In this paper, we propose a metric-based
learning framework followed by a few-shot lesion recognition in WCE data.
Metric-based learning is a meta-learning framework designed to establish
similarity or dissimilarity between concepts while few-shot learning (FSL) aims
to identify new concepts from only a small number of examples. We train a
feature extractor to learn a representation for different small bowel lesions
using metric-based learning. At the testing stage, the category of an unseen
sample is predicted from only a few support examples, thereby allowing the
model to generalize to a new category that has never been seen before. We
demonstrated the efficacy of this method on real patient capsule endoscopy
data.
