Zobrazeno 1 - 10
of 2 483
pro vyhledávání: '"P. A. Fasching"'
Autor:
P. Neven, P. A. Fasching, S. Chia, G. Jerusalem, M. De Laurentiis, S.-A. Im, K. Petrakova, G. V. Bianchi, M. Martín, A. Nusch, G. S. Sonke, L. De la Cruz-Merino, J. T. Beck, J. P. Zarate, Y. Wang, A. Chakravartty, C. Wang, D. J. Slamon
Publikováno v:
Breast Cancer Research, Vol 25, Iss 1, Pp 1-10 (2023)
Abstract Background The phase III MONALEESA-3 trial included first- (1L) and second-line (2L) patients and demonstrated a significant overall survival (OS) benefit for ribociclib + fulvestrant in patients with hormone receptor–positive, human epide
Externí odkaz:
https://doaj.org/article/d5a21e559a8a42ca8ba62ead0fa349e5
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
Autor:
U. G. Poehls, C. C. Hack, A. B. Ekici, M. W. Beckmann, P. A. Fasching, M. Ruebner, H. Huebner
Publikováno v:
European Journal of Medical Research, Vol 23, Iss 1, Pp 1-10 (2018)
Abstract Background Breast cancer screening programs seem to be an insufficient tool for women at high genetic risk for breast cancer. These women are not adequately monitored yet. Genetic testing may improve clearly the quality of breast cancer prev
Externí odkaz:
https://doaj.org/article/8f54c9a2badb4a67818fe3e2fbb87387
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