no code implementations • Cosmin I. Bercea, Olivier Pauly, Andreas K. Maier, Florin C. Ghesu
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.
no code implementations • 11 Apr 2024 • Jun Li, Cosmin I. Bercea, Philip Müller, Lina Felsner, Suhwan Kim, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel
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.
1 code implementation • 21 Mar 2024 • Alicia Durrer, Julia Wolleb, Florentin Bieder, Paul Friedrich, Lester Melie-Garcia, Mario Ocampo-Pineda, Cosmin I. Bercea, Ibrahim E. Hamamci, Benedikt Wiestler, Marie Piraud, Özgür Yaldizli, Cristina Granziera, Bjoern H. Menze, Philippe C. Cattin, Florian Kofler
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.
1 code implementation • 13 Mar 2024 • Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents.
1 code implementation • 19 Jan 2024 • Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel
Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies.
1 code implementation • 14 Dec 2023 • Maxime Di Folco, Cosmin I. Bercea, Julia A. Schnabel
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models.
no code implementations • 26 Aug 2023 • Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel, Andrew P. King
This work presents a novel analysis of biases in unsupervised anomaly detection.
1 code implementation • 31 May 2023 • Cosmin I. Bercea, Michael Neumayr, Daniel Rueckert, Julia A. Schnabel
The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies.
no code implementations • 10 Mar 2023 • Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea, Raja Giryes, Julia A. Schnabel
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.
no code implementations • 8 Jun 2022 • Cosmin I. Bercea, Daniel Rueckert, Julia A. Schnabel
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.
no code implementations • 5 Mar 2021 • Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni
Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.