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.
no code implementations • 18 Mar 2024 • Julia Wolleb, Florentin Bieder, Paul Friedrich, Peter Zhang, Alicia Durrer, Philippe C. Cattin
As diffusion-based methods require a lot of GPU memory and have long sampling times, we present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models.
1 code implementation • 29 Feb 2024 • Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer, Philippe C. Cattin
Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task.
1 code implementation • 27 Mar 2023 • Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkühler, Philippe C. Cattin
Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks.
1 code implementation • 14 Mar 2023 • Paul Friedrich, Julia Wolleb, Florentin Bieder, Florian M. Thieringer, Philippe C. Cattin
Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular.
no code implementations • 14 Mar 2023 • Alicia Durrer, Julia Wolleb, Florentin Bieder, Tim Sinnecker, Matthias Weigel, Robin Sandkühler, Cristina Granziera, Özgür Yaldizli, Philippe C. Cattin
We map images from the source contrast to the target contrast for both directions, from 3 T to 1. 5 T and from 1. 5 T to 3 T. As we only want to change the contrast, not the anatomical information, our method uses the original image to guide the image-to-image translation process by adding structural information.
1 code implementation • 19 Jan 2023 • Florentin Bieder, Julia Wolleb, Robin Sandkühler, Philippe C. Cattin
Most current anomaly detection methods for medical images are based on image reconstruction.
no code implementations • 6 Apr 2022 • Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe C. Cattin
We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output.
2 code implementations • 8 Mar 2022 • Julia Wolleb, Florentin Bieder, Robin Sandkühler, Philippe C. Cattin
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training.
3 code implementations • 6 Dec 2021 • Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, Philippe C. Cattin
By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.
1 code implementation • 13 Oct 2021 • Julia Wolleb, Robin Sandkühler, Florentin Bieder, Muhamed Barakovic, Nouchine Hadjikhani, Athina Papadopoulou, Özgür Yaldizli, Jens Kuhle, Cristina Granziera, Philippe C. Cattin
The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine.
no code implementations • 2 Mar 2021 • Florentin Bieder, Robin Sandkühler, Philippe C. Cattin
Max- and average-pooling are the most popular pooling methods for downsampling in convolutional neural networks.