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pro vyhledávání: '"Rueckert Daniel"'
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
Autor:
Li, Liu, Wang, Hanchun, Baugh, Matthew, Ma, Qiang, Zhang, Weitong, Ouyang, Cheng, Rueckert, Daniel, Kainz, Bernhard
Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst includ
Externí odkaz:
http://arxiv.org/abs/2409.09796
Autor:
Dwedari, Mohammed Munzer, Consagra, William, Müller, Philip, Turgut, Özgün, Rueckert, Daniel, Rathi, Yogesh
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches t
Externí odkaz:
http://arxiv.org/abs/2409.09387