Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain. In the context of the Medical OOD (MOOD) detection challenge 2023, we propose a pipeline that combines a histogram-based method and a diffusion-based method. The histogram-based method is designed to accurately detect homogeneous anomalies in the toy examples of the challenge, such as blobs with constant intensity values. The diffusion-based method is based on one of the latest methods for unsupervised anomaly detection, called DDPM-OOD. We explore this method and propose extensive post-processing steps for pixel-level and sample-level anomaly detection on brain MRI and abdominal CT data prov...
Generative models have been shown to provide a powerful mechanism for anomaly detection by learning ...
Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns proces...
Implementing neural networks for clinical use in medical applications necessitates the ability for t...
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different f...
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Deep Learning models are easily disturbed by variations in the input images that were not observed d...
The detection and localization of anomalies is one important medical image analysis task. Most commo...
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models ...
It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due ...
Deep unsupervised approaches are gathering increased attention for applications such as pathology de...
In medical applications, weakly supervised anomaly detection methods are of great interest, as only ...
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment...
Generative models have been shown to provide a powerful mechanism for anomaly detection by learning ...
Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns proces...
Implementing neural networks for clinical use in medical applications necessitates the ability for t...
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different f...
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deploy...
Deep Learning models are easily disturbed by variations in the input images that were not observed d...
The detection and localization of anomalies is one important medical image analysis task. Most commo...
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models ...
It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due ...
Deep unsupervised approaches are gathering increased attention for applications such as pathology de...
In medical applications, weakly supervised anomaly detection methods are of great interest, as only ...
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment...
Generative models have been shown to provide a powerful mechanism for anomaly detection by learning ...
Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns proces...
Implementing neural networks for clinical use in medical applications necessitates the ability for t...