Zobrazeno 1 - 10
of 2 185
pro vyhledávání: '"Rueckert Daniel"'
Autor:
Hinterwimmer Florian, Consalvo Sarah, Neumann Jan, Micheler Carina, Wilhelm Nikolas, Lang Jan, Eisenhart-Rothe Rüdiger von, Burgkart Rainer, Rueckert Daniel
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 9-12 (2022)
Ewing sarcomas are malignant neoplasm entities typically found in children and adolescents. Early detection is crucial for therapy and prognosis. Due to the low incidence the general experience as well as according data is limited. Novel support tool
Externí odkaz:
https://doaj.org/article/2b1b87b9678b40debaffe3c17f5a174a
Autor:
Bloier Magdalena, Hinterwimmer Florian, Breden Sebastian, Consalvo Sarah, Neumann Jan, Wilhelm Nikolas, Eisenhart-Rothe Rüdiger von, Rueckert Daniel, Burgkart Rainer
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 69-72 (2022)
Bone tumours are a rare and often highly malignant entity. Early clinical diagnosis is the most important step, but the difficulty of detecting and assessing bone malignancies is in its radiological peculiarity and limited experience of non-experts.
Externí odkaz:
https://doaj.org/article/fb759cd762374611aaf6e512add63f85
Employing pre-trained Large Language Models (LLMs) has become the de facto standard in Natural Language Processing (NLP) despite their extensive data requirements. Motivated by the recent surge in research focused on training LLMs with limited data,
Externí odkaz:
http://arxiv.org/abs/2411.09539
Autor:
Lux, Laurin, Berger, Alexander H., Weers, Alexander, Stucki, Nico, Rueckert, Daniel, Bauer, Ulrich, Paetzold, Johannes C.
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topologica
Externí odkaz:
http://arxiv.org/abs/2411.03228
Autor:
Ghoul, Aya, Hammernik, Kerstin, Lingg, Andreas, Krumm, Patrick, Rueckert, Daniel, Gatidis, Sergios, Küstner, Thomas
In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flow and perfusion imaging, and functional assessment of motion patterns
Externí odkaz:
http://arxiv.org/abs/2410.18834
Autor:
Graf, Robert, Hunecke, Florian, Pohl, Soeren, Atad, Matan, Moeller, Hendrik, Starck, Sophie, Kroencke, Thomas, Bette, Stefanie, Bamberg, Fabian, Pischon, Tobias, Niendorf, Thoralf, Schmidt, Carsten, Paetzold, Johannes C., Rueckert, Daniel, Kirschke, Jan S
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition. In this pap
Externí odkaz:
http://arxiv.org/abs/2410.10220
In natural language processing and computer vision, self-supervised pre-training on large datasets unlocks foundational model capabilities across domains and tasks. However, this potential has not yet been realised in time series analysis, where exis
Externí odkaz:
http://arxiv.org/abs/2410.07299
Autor:
Jacob, Athira J, Borgohain, Indraneel, Chitiboi, Teodora, Sharma, Puneet, Comaniciu, Dorin, Rueckert, Daniel
Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image processing tasks for comprehensive assessment of cardiac morphology and fun
Externí odkaz:
http://arxiv.org/abs/2410.01665
Autor:
Schwethelm, Kristian, Kaiser, Johannes, Kuntzer, Jonas, Yigitsoy, Mehmet, Rueckert, Daniel, Kaissis, Georgios
Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. However, its integration with formal privacy-preserving methods, particula
Externí odkaz:
http://arxiv.org/abs/2410.00542
Autor:
Chakravarty, Arunava, Emre, Taha, Lachinov, Dmitrii, Rivail, Antoine, Scholl, Hendrik, Fritsche, Lars, Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) methods for survival analysis are susceptible to image domain shifts ac
Externí odkaz:
http://arxiv.org/abs/2409.20195