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Found 3 papers, 1 papers with code
In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
We advocate that the field should instead focus on the development of simple and efficient detectors that an be combined with effective trackers to allow the implementation of real-time polyp detectors.
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i. e., healthy) images to detect any abnormal (i. e., unhealthy) samples that do not conform to the expected normal patterns.
Ranked #1 on
Anomaly Detection
on LAG
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos.
