Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images.
Paula López DiezJosefine Vilsbøll SundgaardJán MargetaKhassan DiabFrançois PatouRasmus R. PaulsenPublished in: Comput. Medical Imaging Graph. (2024)
Keyphrases
- anomaly detection
- ct images
- deep belief networks
- reinforcement learning
- unsupervised learning
- treatment planning
- traumatic brain injury
- deep learning
- heart disease
- restricted boltzmann machine
- computed tomography
- medical images
- supervised learning
- ct scans
- intrusion detection
- machine learning
- detecting anomalies
- anomalous behavior
- probabilistic model
- network traffic
- chronic obstructive pulmonary disease
- medical imaging
- ct imaging
- region of interest
- learning algorithm
- clinical applications
- pet ct
- computer tomography
- pulmonary nodules
- one class support vector machines
- intrusion detection system
- semi supervised
- clinical data
- pet images
- feature selection
- medical diagnosis
- dimensionality reduction
- pairwise
- imaging modalities
- object recognition
- clinical trials
- training data
- face recognition
- model selection
- medical data
- fracture detection
- neural network