Idrid: Diabetic retinopathy–segmentation and grading challenge
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss,
predominantly affecting the working-age population across the globe. Screening for DR …
predominantly affecting the working-age population across the globe. Screening for DR …
Robust anomaly detection in images using adversarial autoencoders
L Beggel, M Pfeiffer, B Bischl - … September 16–20, 2019, Proceedings, Part …, 2020 - Springer
Reliably detecting anomalies in a given set of images is a task of high practical relevance for
visual quality inspection, surveillance, or medical image analysis. Autoencoder neural …
visual quality inspection, surveillance, or medical image analysis. Autoencoder neural …
Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion …
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in
multiparametric MRI. To compensate for the lack of annotated data adequately sampling the …
multiparametric MRI. To compensate for the lack of annotated data adequately sampling the …
Classification of the epileptic seizure onset zone based on partial annotation
Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical
experts identify the seizure onset zone (SOZ) channel through visual judgment based on …
experts identify the seizure onset zone (SOZ) channel through visual judgment based on …
Emerging ethical considerations for the use of artificial intelligence in ophthalmology
Rapid developments in artificial intelligence (AI) promise improved diagnosis and care for
patients, but raise ethical issues. 1 e5 Over 6 months, in consultation with the American …
patients, but raise ethical issues. 1 e5 Over 6 months, in consultation with the American …
Robust super-resolution GAN, with manifold-based and perception loss
U Upadhyay, SP Awate - 2019 IEEE 16th International …, 2019 - ieeexplore.ieee.org
Super-resolution using deep neural networks typically relies on highly curated training sets
that are often unavailable in clinical deployment scenarios. Using loss functions that assume …
that are often unavailable in clinical deployment scenarios. Using loss functions that assume …
A semi-supervised generalized vae framework for abnormality detection using one-class classification
R Sharma, S Mashkaria… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Anomaly detection is a one-class classification (OCC) problem where the methods learn
either a generative model of the inlier class (eg, in the variants of kernel principal component …
either a generative model of the inlier class (eg, in the variants of kernel principal component …
Rvae-abfa: robust anomaly detection for highdimensional data using variational autoencoder
The curse of dimensionality is a fundamental difficulty in anomaly detection for high
dimensional data. To deal with this problem, the autoencoder based approach is an elegant …
dimensional data. To deal with this problem, the autoencoder based approach is an elegant …
Robust and Uncertainty-Aware VAE (RU-VAE) for One-Class Classification
One-class classification (OCC) methods for abnormality detection learn either a generative
model of the inlier class (eg, using variants of kernel principal component analysis) or a …
model of the inlier class (eg, using variants of kernel principal component analysis) or a …
Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI
Z Alaverdyan - 2019 - hal.science
This work represents one attempt to develop a computer aided diagnosis system for
epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR …
epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR …