Zobrazeno 1 - 10
of 9 572
pro vyhledávání: '"A. Kainz"'
Autor:
Cechnicka, Sarah, Ball, James, Baugh, Matthew, Reynaud, Hadrien, Simmonds, Naomi, Smith, Andrew P. T., Horsfield, Catherine, Roufosse, Candice, Kainz, Bernhard
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods fail to consistently represent the hierarchical structure of WSIs due
Externí odkaz:
http://arxiv.org/abs/2407.13277
Autor:
Marimont, Sergio Naval, Siomos, Vasilis, Baugh, Matthew, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by gener
Externí odkaz:
http://arxiv.org/abs/2407.06635
Autor:
Müller, Johanna P., Kainz, Bernhard
We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily stem from the prevalent reliance on back-propagation for model trainin
Externí odkaz:
http://arxiv.org/abs/2406.14038
Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed, thus data-driven iterative solutions are required, for example when re
Externí odkaz:
http://arxiv.org/abs/2406.13652
Autor:
Li, Zhe, Kainz, Bernhard
Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availab
Externí odkaz:
http://arxiv.org/abs/2406.13536
Existing learning-based cortical surface reconstruction approaches heavily rely on the supervision of pseudo ground truth (pGT) cortical surfaces for training. Such pGT surfaces are generated by traditional neuroimage processing pipelines, which are
Externí odkaz:
http://arxiv.org/abs/2406.12650
Autor:
Reynaud, Hadrien, Meng, Qingjie, Dombrowski, Mischa, Ghosh, Arijit, Day, Thomas, Gomez, Alberto, Leeson, Paul, Kainz, Bernhard
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data. Until now, generative methods have faced constraints in term
Externí odkaz:
http://arxiv.org/abs/2406.00808
Autor:
Ma, Qiang, Liang, Kaili, Li, Liu, Masui, Saga, Guo, Yourong, Nosarti, Chiara, Robinson, Emma C., Kainz, Bernhard, Rueckert, Daniel
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain
Externí odkaz:
http://arxiv.org/abs/2405.08783
Autor:
Starck, Sophie, Sideri-Lampretsa, Vasiliki, Kainz, Bernhard, Menten, Martin, Mueller, Tamara, Rueckert, Daniel
Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical difference
Externí odkaz:
http://arxiv.org/abs/2403.16776
Autor:
Öttl, Mathias, Wilm, Frauke, Steenpass, Jana, Qiu, Jingna, Rübner, Matthias, Hartmann, Arndt, Beckmann, Matthias, Fasching, Peter, Maier, Andreas, Erber, Ramona, Kainz, Bernhard, Breininger, Katharina
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks
Externí odkaz:
http://arxiv.org/abs/2403.14429