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pro vyhledávání: '"Bieder, A."'
Autonomous vehicles require road information for their operation, usually in form of HD maps. Since offline maps eventually become outdated or may only be partially available, online HD map construction methods have been proposed to infer map informa
Externí odkaz:
http://arxiv.org/abs/2411.10316
Autor:
Bieder, Florentin, Friedrich, Paul, Corbaz, Hélène, Durrer, Alicia, Wolleb, Julia, Cattin, Philippe C.
The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the deve
Externí odkaz:
http://arxiv.org/abs/2408.08647
Using HD maps directly as training data for machine learning tasks has seen a massive surge in popularity and shown promising results, e.g. in the field of map perception. Despite that, a standardized HD map framework supporting all parts of map-base
Externí odkaz:
http://arxiv.org/abs/2407.17409
Autor:
Durrer, Alicia, Wolleb, Julia, Bieder, Florentin, Friedrich, Paul, Melie-Garcia, Lester, Ocampo-Pineda, Mario, Bercea, Cosmin I., Hamamci, Ibrahim E., Wiestler, Benedikt, Piraud, Marie, Yaldizli, Özgür, Granziera, Cristina, Menze, Bjoern H., Cattin, Philippe C., Kofler, Florian
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. However, many of the evaluation tools are optimized for analyzing healthy
Externí odkaz:
http://arxiv.org/abs/2403.14499
Autor:
Wolleb, Julia, Bieder, Florentin, Friedrich, Paul, Zhang, Peter, Durrer, Alicia, Cattin, Philippe C.
The high performance of denoising diffusion models for image generation has paved the way for their application in unsupervised medical anomaly detection. As diffusion-based methods require a lot of GPU memory and have long sampling times, we present
Externí odkaz:
http://arxiv.org/abs/2403.11667
Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit the high-dimensional d
Externí odkaz:
http://arxiv.org/abs/2402.19043
Publikováno v:
Medical Imaging with Deep Learning, PMLR 227:552-567, 2024
Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal wi
Externí odkaz:
http://arxiv.org/abs/2303.15288
Autor:
Durrer, Alicia, Wolleb, Julia, Bieder, Florentin, Sinnecker, Tim, Weigel, Matthias, Sandkühler, Robin, Granziera, Cristina, Yaldizli, Özgür, Cattin, Philippe C.
Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longitudinal comparability is essential but can be impaired by these contra
Externí odkaz:
http://arxiv.org/abs/2303.08189
Autor:
Friedrich, Paul, Wolleb, Julia, Bieder, Florentin, Thieringer, Florian M., Cattin, Philippe C.
Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainl
Externí odkaz:
http://arxiv.org/abs/2303.08061
Publikováno v:
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:160-172, 2022
In recent years, anomaly detection has become an essential field in medical image analysis. Most current anomaly detection methods for medical images are based on image reconstruction. In this work, we propose a novel anomaly detection approach based
Externí odkaz:
http://arxiv.org/abs/2301.08064