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Found 9 papers, 4 papers with code
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images
There are few studies that address pathological assessment of endoscopy images in multiclass classification and most of them are based on binary anomaly detection or aim to detect a specific type of anomaly.
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video
Two models were trained using only 78 CE and 27 WCE annotated frames to predict the location of 25700 and 1825 video frames from CE and WCE, respectively.
Automatic classification of multiple catheters in neonatal radiographs with deep learning
Performance was similar for the set of 58 test images consisting of 2 or more catheters, with an AP of 0. 975 (0. 255-1. 000) for NGTs, 0. 997 (0. 009-1. 000) for ETTs, 0. 981 (0. 797-0. 998) for UACs, and 0. 937 (0. 689-0. 990) for UVCs.
Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?
Catheters are the second most common abnormal finding on radiographs.
Deep Learning for Low-Dose CT Denoising
Low-dose CT denoising is a challenging task that has been studied by many researchers.
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
In this work, we proposed a simple way of synthesizing catheters on X-ray images and a scale recurrent network for catheter detection.
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Melanoma is a curable aggressive skin cancer if detected early.
Sharpness-aware Low dose CT denoising using conditional generative adversarial network
Low Dose Computed Tomography (LDCT) has offered tremendous benefits in radiation restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance.
