Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Jaeger, Paul F."'
Autor:
Traub, Jeremias, Bungert, Till J., Lüth, Carsten T., Baumgartner, Michael, Maier-Hein, Klaus H., Maier-Hein, Lena, Jaeger, Paul F
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these sys
Externí odkaz:
http://arxiv.org/abs/2407.01032
Autor:
Zenk, Maximilian, Zimmerer, David, Isensee, Fabian, Traub, Jeremias, Norajitra, Tobias, Jäger, Paul F., Maier-Hein, Klaus
Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a signifi
Externí odkaz:
http://arxiv.org/abs/2406.03323
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:
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:
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
Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other
Externí odkaz:
http://arxiv.org/abs/2401.08501
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative r
Externí odkaz:
http://arxiv.org/abs/2311.13964
Autor:
Adler, Tim J., Nölke, Jan-Hinrich, Reinke, Annika, Tizabi, Minu Dietlinde, Gruber, Sebastian, Trofimova, Dasha, Ardizzone, Lynton, Jaeger, Paul F., Buettner, Florian, Köthe, Ullrich, Maier-Hein, Lena
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible
Externí odkaz:
http://arxiv.org/abs/2309.09764
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