Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Ulrich, Constantin"'
Autor:
Ulrich, Constantin, Knobloch, Catherine, Holzschuh, Julius C., Wald, Tassilo, Rokuss, Maximilian R., Zenk, Maximilian, Fischer, Maximilian, Baumgartner, Michael, Isensee, Fabian, Maier-Hein, Klaus H.
Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust
Externí odkaz:
http://arxiv.org/abs/2404.15718
Autor:
Isensee, Fabian, Wald, Tassilo, Ulrich, Constantin, Baumgartner, Michael, Roy, Saikat, Maier-Hein, Klaus, Jaeger, Paul F.
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results. Despite this, the pursuit of novel architectures, and the respe
Externí odkaz:
http://arxiv.org/abs/2404.09556
Autor:
Kirchhoff, Yannick, Rokuss, Maximilian R., Roy, Saikat, Kovacs, Balint, Ulrich, Constantin, Wald, Tassilo, Zenk, Maximilian, Vollmuth, Philipp, Kleesiek, Jens, Isensee, Fabian, Maier-Hein, Klaus
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overl
Externí odkaz:
http://arxiv.org/abs/2404.03010
Autor:
Luo, Xiangde, Fu, Jia, Zhong, Yunxin, Liu, Shuolin, Han, Bing, Astaraki, Mehdi, Bendazzoli, Simone, Toma-Dasu, Iuliana, Ye, Yiwen, Chen, Ziyang, Xia, Yong, Su, Yanzhou, Ye, Jin, He, Junjun, Xing, Zhaohu, Wang, Hongqiu, Zhu, Lei, Yang, Kaixiang, Fang, Xin, Wang, Zhiwei, Lee, Chan Woong, Park, Sang Joon, Chun, Jaehee, Ulrich, Constantin, Maier-Hein, Klaus H., Ndipenoch, Nchongmaje, Miron, Alina, Li, Yongmin, Zhang, Yimeng, Chen, Yu, Bai, Lu, Huang, Jinlong, An, Chengyang, Wang, Lisheng, Huang, Kaiwen, Gu, Yunqi, Zhou, Tao, Zhou, Mu, Zhang, Shichuan, Liao, Wenjun, Wang, Guotai, Zhang, Shaoting
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. P
Externí odkaz:
http://arxiv.org/abs/2312.09576
Autor:
Koehler, Gregor, Wald, Tassilo, Ulrich, Constantin, Zimmerer, David, Jaeger, Paul F., Franke, Jörg K. H., Kohl, Simon, Isensee, Fabian, Maier-Hein, Klaus H.
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initi
Externí odkaz:
http://arxiv.org/abs/2309.07513
Autor:
Wald, Tassilo, Ulrich, Constantin, Isensee, Fabian, Zimmerer, David, Koehler, Gregor, Baumgartner, Michael, Maier-Hein, Klaus H.
Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common failure modes. Previous attempts focusing on decorrelation of output pr
Externí odkaz:
http://arxiv.org/abs/2307.02516
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:
Ulrich, Constantin, Isensee, Fabian, Wald, Tassilo, Zenk, Maximilian, Baumgartner, Michael, Maier-Hein, Klaus H.
The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervi
Externí odkaz:
http://arxiv.org/abs/2303.14444
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
Semantic segmentation is one of the most popular research areas in medical image computing. Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety
Externí odkaz:
http://arxiv.org/abs/2208.10791