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Application of Structural Similarity Analysis of Visually Salient Areas and Hierarchical Clustering in the Screening of Similar Wireless Capsule Endoscopic Images
Small intestinal capsule endoscopy is the mainstream method for inspecti...
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Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Few-shot learning is an important area of research. Conceptually, humans...
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WCE Polyp Detection with Triplet based Embeddings
Wireless capsule endoscopy is a medical procedure used to visualize the ...
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Classification of glomerular hypercellularity using convolutional features and support vector machine
Glomeruli are histological structures of the kidney cortex formed by int...
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Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames
Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of g...
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Few Shot Speaker Recognition using Deep Neural Networks
The recent advances in deep learning are mostly driven by availability o...
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Automated polyp detection in colon capsule endoscopy
Colorectal polyps are important precursors to colon cancer, a major heal...
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Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video
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.
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