7 citations · 7 across the 2 of their papers we have counts for
7 papers
Reliable Mislabel Detection for Video Capsule Endoscopy Data
Julia Werner, Julius Oexle, Oliver Bause +5
The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however, obtaining such datasets is pa…
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
Julia Werner, Christoph Gerum, Moritz Reiber +2
This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Co…
Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy
Oliver Bause, Jörg Gamerdinger, Julia Werner +1
Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key challenge is their limited siz…
Seeing More with Less: Video Capsule Endoscopy with Multi-Task Learning
Julia Werner, Oliver Bause, Julius Oexle +4
Video capsule endoscopy has become increasingly important for investigating the small intestine within the gastrointestinal tract. However, a persistent challenge remains the short…
Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies
Julia Werner, Christoph Gerum, Jorg Nick +4
Capsule endoscopy is a method to capture images of the gastrointestinal tract and screen for diseases which might remain hidden if investigated with standard endoscopes. Due to the…
Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation
Oliver Bause, Julia Werner, Paul Palomero Bernardo +1
For many real-world applications involving low-power sensor edge devices deep neural networks used for image classification might not be suitable. This is due to their typically la…