Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation

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

The Reality in the Surveillance of Breast Cancer Survivors—Results of a Patient Survey

Autor: Stemmler Hans-Joachim, Lässig Dorit, Stieber Petra, Bauerfeind Ingo, Kahlert Steffen, Fasching Peter Alexander, Beckmann Matthias Wilhelm, Glattes Margrit, Goldmann-Posch Ursula, Hoffmann Verena, Untch Michael, Heinemann Volker

Publikováno v: Breast Cancer: Basic and Clinical Research, Vol 1, Pp 17-23 (2008)

Background: International guidelines for the surveillance of breast cancer patients recommend a minimized clinical follow-up including routine history and physical examination and regularly scheduled mammograms. However, the abandonment of scheduled

Externí odkaz: https://doaj.org/article/6575da06dff7435fa418e970e398fd26

Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation

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

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

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

Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models

Autor: Wu, Zhiliang, Yang, Yinchong, Fasching, Peter A., Tresp, Volker

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