Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Felsner, Lina"'
Autor:
Kiechle, Johannes, Lang, Daniel M., Fischer, Stefan M., Felsner, Lina, Peeken, Jan C., Schnabel, Julia A.
Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to
Externí odkaz:
http://arxiv.org/abs/2407.17219
Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
Autor:
Fischer, Stefan M., Felsner, Lina, Osuala, Richard, Kiechle, Johannes, Lang, Daniel M., Peeken, Jan C., Schnabel, Julia A.
In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually i
Externí odkaz:
http://arxiv.org/abs/2407.07853
Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Param
Externí odkaz:
http://arxiv.org/abs/2407.04355
Autor:
Li, Jun, Kim, Su Hwan, Müller, Philip, Felsner, Lina, Rueckert, Daniel, Wiestler, Benedikt, Schnabel, Julia A., Bercea, Cosmin I.
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the interpretability of anomaly detection maps? and (2) Can anomaly maps im
Externí odkaz:
http://arxiv.org/abs/2404.07622
Publikováno v:
7th International Conference on Image Formation in X-Ray Computed Tomography, Proc. Vol. 12304 (2022)
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, they achieve high reconstruction performance while ensuring consistency
Externí odkaz:
http://arxiv.org/abs/2201.07562
Autor:
Roser, Philipp, Birkhold, Annette, Preuhs, Alexander, Syben, Christopher, Felsner, Lina, Hoppe, Elisabeth, Strobel, Norbert, Korwarschik, Markus, Fahrig, Rebecca, Maier, Andreas
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based image translation approaches yielded promising results. As there are no physics constraints ap
Externí odkaz:
http://arxiv.org/abs/2101.09177
Autor:
Hoppe, Elisabeth, Wetzl, Jens, Roser, Philipp, Felsner, Lina, Preuhs, Alexander, Maier, Andreas
Continuous protocols for cardiac magnetic resonance imaging enable sampling of the cardiac anatomy simultaneously resolved into cardiac phases. To avoid respiration artifacts, associated motion during the scan has to be compensated for during reconst
Externí odkaz:
http://arxiv.org/abs/2012.13700
Autor:
Phair, Andrew, Fotaki, Anastasia, Felsner, Lina, Fletcher, Thomas J., Qi, Haikun, Botnar, René M., Prieto, Claudia
Publikováno v:
In Journal of Cardiovascular Magnetic Resonance Summer 2024 26(1)
Autor:
Felsner, Lina, Würfl, Tobias, Syben, Christopher, Roser, Philipp, Preuhs, Alexander, Maier, Andreas, Riess, Christian
The reconstruction problem of voxels with individual weightings can be modeled a position- and angle- dependent function in the forward-projection. This changes the system matrix and prohibits to use standard filtered backprojection. In this work we
Externí odkaz:
http://arxiv.org/abs/2010.14205
Autor:
Hu, Shiyang, Felsner, Lina, Maier, Andreas, Ludwig, Veronika, Anton, Gisela, Riess, Christian
Talbot-Lau X-ray phase-contrast imaging is a novel imaging modality, which provides not only an X-ray absorption image, but also additionally a differential phase image and a dark-field image. The dark-field image is related to small angle scattering
Externí odkaz:
http://arxiv.org/abs/1811.04457