Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Xiao, Shuhan"'
Autor:
Kovacs, Balint, Xiao, Shuhan, Rokuss, Maximilian, Ulrich, Constantin, Isensee, Fabian, Maier-Hein, Klaus H.
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images through data quality and handling strategies. In response, we developed t
Externí odkaz:
http://arxiv.org/abs/2409.10120
Autor:
Rokuss, Maximilian, Kovacs, Balint, Kirchhoff, Yannick, Xiao, Shuhan, Ulrich, Constantin, Maier-Hein, Klaus H., Isensee, Fabian
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological variability, different tracers used in PET imaging, and diverse imaging
Externí odkaz:
http://arxiv.org/abs/2409.09478
Autor:
Fischer, Maximilian, Neher, Peter, Wald, Tassilo, Almeida, Silvia Dias, Xiao, Shuhan, Schüffler, Peter, Braren, Rickmer, Götz, Michael, Muckenhuber, Alexander, Kleesiek, Jens, Nolden, Marco, Maier-Hein, Klaus
Processing histopathological Whole Slide Images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently possible without substantially
Externí odkaz:
http://arxiv.org/abs/2406.12623
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:
Gotkowski, Karol, Lüth, Carsten, Jäger, Paul F., Ziegler, Sebastian, Krämer, Lars, Denner, Stefan, Xiao, Shuhan, Disch, Nico, Maier-Hein, Klaus H., Isensee, Fabian
Traditionally, segmentation algorithms require dense annotations for training, demanding significant annotation efforts, particularly within the 3D medical imaging field. Scribble-supervised learning emerges as a possible solution to this challenge,
Externí odkaz:
http://arxiv.org/abs/2403.12834
Autor:
Denner, Stefan, Zimmerer, David, Bounias, Dimitrios, Bujotzek, Markus, Xiao, Shuhan, Kausch, Lisa, Schader, Philipp, Penzkofer, Tobias, Jäger, Paul F., Maier-Hein, Klaus
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. In res
Externí odkaz:
http://arxiv.org/abs/2403.06567
Akademický článek
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Autor:
Pei, Yuhou, Gu, Wen, Cheng, Shuo, Xiao, Shuhan, Wang, Chunling, Yang, Yang, Zhong, Heng, Jin, Fangming
Publikováno v:
ACS Catalysis; 9/15/2023, Vol. 13 Issue 18, p12082-12091, 10p
Akademický článek
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