Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches
R Hassanaly, C Brianceau, O Colliot… - … Conference on Medical …, 2023 - Springer
The use of deep generative models for unsupervised anomaly detection is an area of
research that has gained interest in recent years in the field of medical imaging. Among all …
research that has gained interest in recent years in the field of medical imaging. Among all …
A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023
Almost 85% of companies polled said they were looking into anomaly detection (AD)
technologies for their industrial image anomalies. The present problem concerns detecting …
technologies for their industrial image anomalies. The present problem concerns detecting …
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging
data as it allows identifying a wide variety of anomalies from unlabelled data. It relies on …
data as it allows identifying a wide variety of anomalies from unlabelled data. It relies on …
Pseudo-healthy image reconstruction with variational autoencoders for anomaly detection: A benchmark on 3D brain FDG PET
Many deep generative models have been proposed to reconstruct pseudo-healthy images
for anomaly detection. Among these models, the variational autoencoder (VAE) has …
for anomaly detection. Among these models, the variational autoencoder (VAE) has …
Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learning
R Hassanaly, C Brianceau, M Diaz… - Medical Imaging …, 2024 - spiedigitallibrary.org
In this paper, we present ClinicaDL, an open-source software platform that aims at
enhancing the reproducibility and rigor of research for deep learning in neuroimaging. We …
enhancing the reproducibility and rigor of research for deep learning in neuroimaging. We …
Endüstriyel kontrol sistemlerinde anomali tespiti
EH Demircioğlu - 2024 - acikerisim.uludag.edu.tr
Endüstriyel sistemlerde oluşan büyük veri bu sistemler için kontrol ve izleme araçlarının
geliştirilmesi süreçlerinde önemli zorluklar oluşturmaktadır. Son yıllarda, endüstriyel …
geliştirilmesi süreçlerinde önemli zorluklar oluşturmaktadır. Son yıllarda, endüstriyel …