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Found 6 papers, 2 papers with code
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
Our experiments and results in the latest MOOD challenge show that our simple yet effective approach can substantially improve the performance of Out-of-Distribution detection techniques which rely on synthetic anomalies.
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy.
Implicit U-Net for volumetric medical image segmentation
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images.
Implicit field learning for unsupervised anomaly detection in medical images
In our approach, an auto-decoder feed-forward neural network learns the distribution of healthy images in the form of a mapping between spatial coordinates and probabilities over a proxy for tissue types.
Out-of-Distribution Detection
Unsupervised Anomaly Detection
Anomaly detection through latent space restoration using vector-quantized variational autoencoders
The prior distribution of latent codes is then modelled using an Auto-Regressive (AR) model.
Out-of-Distribution Detection
Unsupervised Anomaly Detection
