Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
Review on deep learning approaches for anomaly event detection in video surveillance
In the last few years, due to the continuous advancement of technology, human behavior
detection and recognition have become important scientific research in the field of computer …
detection and recognition have become important scientific research in the field of computer …
UTRAD: Anomaly detection and localization with U-transformer
Anomaly detection is an active research field in industrial defect detection and medical
disease detection. However, previous anomaly detection works suffer from unstable training …
disease detection. However, previous anomaly detection works suffer from unstable training …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
Anomaly detection based on weighted fuzzy-rough density
The density-based method is a more widely used anomaly detection. However, most of the
existing density-based methods mainly focus on dealing with certainty data and do not …
existing density-based methods mainly focus on dealing with certainty data and do not …
[HTML][HTML] Going deep into schizophrenia with artificial intelligence
Despite years of research, the mechanisms governing the onset, relapse, symptomatology,
and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to …
and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to …
MOCCA: Multilayer one-class classification for anomaly detection
Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to
incomplete knowledge about the data distribution or an unknown process that suddenly …
incomplete knowledge about the data distribution or an unknown process that suddenly …
Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals
Recently, researchers in the biomedical community have introduced deep learning-based
epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate …
epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate …
Knowledge-preserving continual person re-identification using graph attention network
Abstract Person re-identification (ReID), considered as a sub-problem of image retrieval, is
critical for intelligent security. The general practice is to train a deep model on images from a …
critical for intelligent security. The general practice is to train a deep model on images from a …
Efficient time series anomaly detection by multiresolution self-supervised discriminative network
Time series anomaly detection aims to identify abnormal subsequences in time series that
are markedly different from the temporal behaviors of the entire sequence. Although …
are markedly different from the temporal behaviors of the entire sequence. Although …