Zobrazeno 1 - 10
of 3 173
pro vyhledávání: '"Fasching, Peter A"'
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:
Thomssen, Christoph, Lüftner, Diana, Untch, Michael, Haidinger, Renate, Würstlein, Rachel, Harbeck, Nadia, Augustin, Doris, Briest, Susanne, Ettl, Johannes, Fasching, Peter A., Förster, Frank, Kurbacher, Christian M., Lück, Hans-Joachim, Marschner, Norbert, Müller, Lothar, Müller, Volkmar, Perlova-Griff, Lidia, Radke, Isabel, Ruckhäberle, Eugen, Scheffen, Iris, Schumacher-Wulf, Eva, Schwoerer, Moritz, Steinfeld-Birg, Dieter, Ziegler-Löhr, Katja
The 5th International Consensus Conference for Advanced Breast Cancer (ABC5) took place on November 14–16, 2019, in Lisbon, Portugal. Its aim is to standardize the treatment of advanced breast cancer based on the available evidence and to ensure th
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A83638
https://ul.qucosa.de/api/qucosa%3A83638/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A83638/attachment/ATT-0/
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
Autor:
Schmidt, Carolin, Stöhr, Robert, Dimitrova, Lora, Beckmann, Matthias W., Rübner, Matthias, Fasching, Peter A., Denkert, Carsten, Lehmann, Ulrich, Vollbrecht, Claudia, Haller, Florian, Hartmann, Arndt, Erber, Ramona
Publikováno v:
In The Journal of Molecular Diagnostics July 2024 26(7):624-637
Autor:
Dareng, Eileen O., Coetzee, Simon G., Tyrer, Jonathan P., Peng, Pei-Chen, Rosenow, Will, Chen, Stephanie, Davis, Brian D., Dezem, Felipe Segato, Seo, Ji-Heui, Nameki, Robbin, Reyes, Alberto L., Aben, Katja K.H., Anton-Culver, Hoda, Antonenkova, Natalia N., Aravantinos, Gerasimos, Bandera, Elisa V., Beane Freeman, Laura E., Beckmann, Matthias W., Beeghly-Fadiel, Alicia, Benitez, Javier, Bernardini, Marcus Q., Bjorge, Line, Black, Amanda, Bogdanova, Natalia V., Bolton, Kelly L., Brenton, James D., Budzilowska, Agnieszka, Butzow, Ralf, Cai, Hui, Campbell, Ian, Cannioto, Rikki, Chang-Claude, Jenny, Chanock, Stephen J., Chen, Kexin, Chenevix-Trench, Georgia, Chiew, Yoke-Eng, Cook, Linda S., DeFazio, Anna, Dennis, Joe, Doherty, Jennifer A., Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M., Ene, Gabrielle, Fasching, Peter A., Flanagan, James M., Fortner, Renée T., Fostira, Florentia, Gentry-Maharaj, Aleksandra, Giles, Graham G., Goodman, Marc T., Gronwald, Jacek, Haiman, Christopher A., Håkansson, Niclas, Heitz, Florian, Hildebrandt, Michelle A.T., Høgdall, Estrid, Høgdall, Claus K., Huang, Ruea-Yea, Jensen, Allan, Jones, Michael E., Kang, Daehee, Karlan, Beth Y., Karnezis, Anthony N., Kelemen, Linda E., Kennedy, Catherine J., Khusnutdinova, Elza K., Kiemeney, Lambertus A., Kjaer, Susanne K., Kupryjanczyk, Jolanta, Labrie, Marilyne, Lambrechts, Diether, Larson, Melissa C., Le, Nhu D., Lester, Jenny, Li, Lian, Lubiński, Jan, Lush, Michael, Marks, Jeffrey R., Matsuo, Keitaro, May, Taymaa, McLaughlin, John R., McNeish, Iain A., Menon, Usha, Missmer, Stacey, Modugno, Francesmary, Moffitt, Melissa, Monteiro, Alvaro N., Moysich, Kirsten B., Narod, Steven A., Nguyen-Dumont, Tu, Odunsi, Kunle, Olsson, Håkan, Onland-Moret, N. Charlotte, Park, Sue K., Pejovic, Tanja, Permuth, Jennifer B., Piskorz, Anna, Prokofyeva, Darya, Riggan, Marjorie J., Risch, Harvey A., Rodríguez-Antona, Cristina, Rossing, Mary Anne, Sandler, Dale P., Setiawan, V. Wendy, Shan, Kang, Song, Honglin, Southey, Melissa C., Steed, Helen, Sutphen, Rebecca, Swerdlow, Anthony J., Teo, Soo Hwang, Terry, Kathryn L., Thompson, Pamela J., Vestrheim Thomsen, Liv Cecilie, Titus, Linda, Trabert, Britton, Travis, Ruth, Tworoger, Shelley S., Valen, Ellen, Van Nieuwenhuysen, Els, Edwards, Digna Velez, Vierkant, Robert A., Webb, Penelope M., Weinberg, Clarice R., Weise, Rayna Matsuno, Wentzensen, Nicolas, White, Emily, Winham, Stacey J., Wolk, Alicja, Woo, Yin-Ling, Wu, Anna H., Yan, Li, Yannoukakos, Drakoulis, Zeinomar, Nur, Zheng, Wei, Ziogas, Argyrios, Berchuck, Andrew, Goode, Ellen L., Huntsman, David G., Pearce, Celeste L., Ramus, Susan J., Sellers, Thomas A., Freedman, Matthew L., Lawrenson, Kate, Schildkraut, Joellen M., Hazelett, Dennis, Plummer, Jasmine T., Kar, Siddhartha, Jones, Michelle R., Pharoah, Paul D.P., Gayther, Simon A.
Publikováno v:
In The American Journal of Human Genetics 6 June 2024 111(6):1061-1083
Autor:
Fasching, Peter A., Hack, Carolin C., Nabieva, Naiba, Maass, Nicolai, Aktas, Bahriye, Kümmel, Sherko, Thomssen, Christoph, Wolf, Christopher, Kolberg, Hans-Christian, Brucker, Cosima, Janni, Wolfgang, Dall, Peter, Schneeweiss, Andreas, Marme, Frederik, Sütterlin, Marc W., Ruebner, Matthias, Theuser, Anna-Katharin, Kellner, Sara, Hofmann, Nadine M., Böhm, Sybille, Almstedt, Katrin, Lück, Hans-Joachim, Schmatloch, Sabine, Kalder, Matthias, Uleer, Christoph, Jurhasz-Böss, Ingolf, Hanf, Volker, Jackisch, Christian, Müller, Volkmar, Rack, Brigitte, Belleville, Erik, Wallwiener, Diethelm, Rody, Achim, Rauh, Claudia, Bayer, Christian M., Uhrig, Sabrina, Goossens, Chloë, Huebner, Hanna, Brucker, Sara Y., Hein, Alexander, Fehm, Tanja N., Häberle, Lothar
Publikováno v:
In European Journal of Cancer September 2024 209
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