Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter …
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal …
SN Marimont, G Tarroni - 2021 IEEE 18th International …, 2021 - ieeexplore.ieee.org
We propose an out-of-distribution detection method that combines density and restoration- based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ …
We propose a novel unsupervised out-of-distribution detection method for medical images based on implicit fields image representations. In our approach, an auto-decoder feed …
Abstract Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most …
Many current state-of-the-art methods for anomaly detection in medical images rely on calculating a residual image between a potentially anomalous input image and its (" …
Artificial intelligence (AI) image translation has been a valuable tool for processing image data in biological and medical research. To apply such a tool in mission-critical applications …
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is …
T Peng, Y Zheng, L Zhao, E Zheng - Sensors, 2024 - mdpi.com
The occurrence of anomalies on the surface of industrial products can lead to issues such as decreased product quality, reduced production efficiency, and safety hazards. Early …