Zobrazeno 1 - 10
of 1 692
pro vyhledávání: '"Petersen, Jens A"'
Autor:
Xiao, Shuhan, Klein, Lukas, Petersen, Jens, Vollmuth, Philipp, Jaeger, Paul F., Maier-Hein, Klaus H.
Identifying predictive biomarkers, which forecast individual treatment effectiveness, is crucial for personalized medicine and informs decision-making across diverse disciplines. These biomarkers are extracted from pre-treatment data, often within ra
Externí odkaz:
http://arxiv.org/abs/2406.02534
Autor:
Habibian, Amirhossein, Ghodrati, Amir, Fathima, Noor, Sautiere, Guillaume, Garrepalli, Risheek, Porikli, Fatih, Petersen, Jens
This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step, we identify that not all operations are equally relevant for th
Externí odkaz:
http://arxiv.org/abs/2312.08128
Autor:
Kutnár, Denis, Vogelius, Ivan R, Håkansson, Katrin Elisabet, Petersen, Jens, Friborg, Jeppe, Specht, Lena, Bernsdorf, Mogens, Gothelf, Anita, Kristensen, Claus, Smith, Abraham George
Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes based on pretreatment imaging is key to biologically targeted radiation t
Externí odkaz:
http://arxiv.org/abs/2308.08396
HORTENSIA, a program package for the simulation of nonadiabatic autoionization dynamics in molecules
We present a program package for the simulation of ultrafast vibration-induced autoionization dynamics in molecular anions in the manifold of the adiabatic anionic states and the discretized ionization continuum. This program, called HORTENSIA ($\und
Externí odkaz:
http://arxiv.org/abs/2307.04437
Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation n
Externí odkaz:
http://arxiv.org/abs/2304.04606
Autor:
Roy, Saikat, Koehler, Gregor, Baumgartner, Michael, Ulrich, Constantin, Petersen, Jens, Isensee, Fabian, Maier-Hein, Klaus
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their rel
Externí odkaz:
http://arxiv.org/abs/2304.04225
Autor:
Roy, Saikat, Koehler, Gregor, Ulrich, Constantin, Baumgartner, Michael, Petersen, Jens, Isensee, Fabian, Jaeger, Paul F., Maier-Hein, Klaus
There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging.
Externí odkaz:
http://arxiv.org/abs/2303.09975
Autor:
Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., Maier-Hein, Lena
Publikováno v:
Nature methods, 1-13 (2024)
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in im
Externí odkaz:
http://arxiv.org/abs/2302.01790
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images
Externí odkaz:
http://arxiv.org/abs/2301.05489
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for dee
Externí odkaz:
http://arxiv.org/abs/2301.05465