Zobrazeno 1 - 10
of 2 842
pro vyhledávání: '"P. Fasching"'
Autor:
S. Kümmel, P. Schmid, N. Harbeck, M. Takahashi, M. Untch, J.-F. Boileau, J. Cortes, H. McArthur, R. Dent, J. O’Shaughnessy, L. Pusztai, T. Foukakis, Y.H. Park, R. Hui, F. Cardoso, C. Denkert, Y. Zhu, W. Pan, V. Karantza, P. Fasching
Publikováno v:
Breast, Vol 68, Iss , Pp S63-S64 (2023)
Externí odkaz:
https://doaj.org/article/df7da6e644f749229398d191714cb09f
Autor:
Bauer-Fasching, B., Bernhard, K., Brändli, E., Burger, H., Eisele, B., Hümmerich, S., Neuhold, J., Paunzen, E., Piecka, M., Ratzenböck, S., Prišegen, M.
The manifestation of surface spots on magnetic chemically peculiar (mCP) stars is most commonly explained by the atomic diffusion theory, which requires a calm stellar atmosphere and only moderate rotation. While very successful and well described, t
Externí odkaz:
http://arxiv.org/abs/2406.06203
Autor:
Öttl, Mathias, Mei, Siyuan, Wilm, Frauke, Steenpass, Jana, Rübner, Matthias, Hartmann, Arndt, Beckmann, Matthias, Fasching, Peter, Maier, Andreas, Erber, Ramona, Breininger, Katharina
Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions
Externí odkaz:
http://arxiv.org/abs/2403.14440
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
Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty. Because these c
Externí odkaz:
http://arxiv.org/abs/2306.00176
Autor:
Prabakaran, Bharath Srinivas, Fasching, Felix, Schreib, Juri, Steininger, Andreas, Shafique, Muhammad
Bluetooth (BT) has revolutionized close-range communication enabling smart capabilities in everyday devices through wireless technology. One of the most important sub-domains of Internet-of-Things (IoT) specializes in the usage of BT technologies to
Externí odkaz:
http://arxiv.org/abs/2212.10289
Autor:
Öttl, Mathias, Mönius, Jana, Rübner, Matthias, Geppert, Carol I., Qiu, Jingna, Wilm, Frauke, Hartmann, Arndt, Beckmann, Matthias W., Fasching, Peter A., Maier, Andreas, Erber, Ramona, Breininger, Katharina
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to
Externí odkaz:
http://arxiv.org/abs/2211.06150
Autor:
Öttl, Mathias, Mönius, Jana, Marzahl, Christian, Rübner, Matthias, Geppert, Carol I., Hartmann, Arndt, Beckmann, Matthias W., Fasching, Peter, Maier, Andreas, Erber, Ramona, Breininger, Katharina
Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however, the manual annotation of such data is extremely laborious. In this wo
Externí odkaz:
http://arxiv.org/abs/2201.07572
Electronic Health Records often suffer from missing data, which poses a major problem in clinical practice and clinical studies. A novel approach for dealing with missing data are Generative Adversarial Nets (GANs), which have been generating huge re
Externí odkaz:
http://arxiv.org/abs/2108.01701
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy and neglect prediction uncertainty. We propose Deep Kernel Accelerate
Externí odkaz:
http://arxiv.org/abs/2107.12250