Search Results for author:
Found 11 papers, 5 papers with code
SHAMANN: Shared Memory Augmented Neural Networks
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping.
Multi-Image Visual Question Answering for Unsupervised Anomaly Detection
To the best of our knowledge, we are the first to leverage a language model for unsupervised anomaly detection, for which we construct a dataset with different questions and answers.
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e. g., for the evaluation of volumetric changes.
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents.
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies.
Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models.
Bias in Unsupervised Anomaly Detection in Brain MRI
This work presents a novel analysis of biases in unsupervised anomaly detection.
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies.
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition.
What do we learn? Debunking the Myth of Unsupervised Outlier Detection
We have found that state-of-the-art (SOTA) AEs are either unable to constrain the latent manifold and allow reconstruction of abnormal patterns, or they are failing to accurately restore the inputs from their latent distribution, resulting in blurred or misaligned reconstructions.
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.
