Zobrazeno 1 - 10
of 3 398
pro vyhledávání: '"Hartmann Arndt"'
Autor:
Heinlein Lukas, Benz Michaela, Kuritcyn Petr, Bruns Volker, Hartmann Arndt, Keil Felix, Geppert Carol, Evert Katja, Wittenberg Thomas
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 166-169 (2023)
Background: The examination of lymph nodes (LNs) regarding metastases is vital for the staging of cancer patients, which is necessary for diagnosis and adequate treatment selection. Advancements in digital pathology, utilizing Whole-Slide Images (WSI
Externí odkaz:
https://doaj.org/article/5e3f07f60dad4f0cbd9b9ead58a31877
Autor:
Kletzander Rosalie, Kuritcyn Petr, Bruns Volker, Eckstein Markus, Geppert Carol, Hartmann Arndt, Benz Michaela
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 491-494 (2023)
Few-shot learning addresses the problem of classification when little data or few labels are available. This is especially relevant in histopathology, where labeling must be carried out by highly trained medical experts. Prototypical Networks promise
Externí odkaz:
https://doaj.org/article/ab3d9771c5d447de8354813a19560576
Autor:
Dexl Jakob, Benz Michaela, Kuritcyn Petr, Wittenberg Thomas, Bruns Volker, Geppert Carol, Hartmann Arndt, Bischl Bernd, Goschenhofer Jann
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 344-347 (2022)
We explore the task of tissue classification for colon cancer histology in a low label regime comparing a semi-supervised and a supervised learning strategy in a series of experiments. Further, we investigate the model robustness w.r.t. distribution
Externí odkaz:
https://doaj.org/article/3a436ac07def4f1294c9db3081cb1688
Autor:
Öttl, Mathias, Mei, Siyuan, Wilm, Frauke, Steenpass, Jana, Rübner, Matthias, Hartmann, Arndt, Beckmann, Matthias, Fasching, Peter, Maier, Andreas, Erber, Ramona, Breininger, Katharina
Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions
Externí odkaz:
http://arxiv.org/abs/2403.14440
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
Autor:
Öttl, Mathias, Mönius, Jana, Rübner, Matthias, Geppert, Carol I., Qiu, Jingna, Wilm, Frauke, Hartmann, Arndt, Beckmann, Matthias W., Fasching, Peter A., Maier, Andreas, Erber, Ramona, Breininger, Katharina
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to
Externí odkaz:
http://arxiv.org/abs/2211.06150
Autor:
Krebs, Markus, Haller, Florian, Spörl, Silvia, Gerhard-Hartmann, Elena, Utpatel, Kirsten, Maurus, Katja, Kunzmann, Volker, Chatterjee, Manik, Venkataramani, Vivek, Maatouk, Imad, Bittrich, Max, Einwag, Tatjana, Meidenbauer, Norbert, Tögel, Lars, Hirsch, Daniela, Dietmaier, Wolfgang, Keil, Felix, Scheiter, Alexander, Immel, Alexander, Heudobler, Daniel, Einhell, Sabine, Kaiser, Ulrich, Sedlmeier, Anja M., Maurer, Julia, Schenkirsch, Gerhard, Jordan, Frank, Schmutz, Maximilian, Dintner, Sebastian, Rosenwald, Andreas, Hartmann, Arndt, Evert, Matthias, Märkl, Bruno, Bargou, Ralf, Mackensen, Andreas, Beckmann, Matthias W., Pukrop, Tobias, Herr, Wolfgang, Einsele, Hermann, Trepel, Martin, Goebeler, Maria-Elisabeth, Claus, Rainer, Kerscher, Alexander, Lüke, Florian
Publikováno v:
In European Journal of Cancer August 2024 207
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
Autor:
Heidkamp Gordon F, Eissing Nathalie, Heger Lukas, Cesnjevar Robert, Hartmann Arndt, Zenk Johannes, Ulrich Evelyn, Mackensen Andreas, Schuler Gerold, Schauf Burkhard, Nimmerjahn Falk, Dudziak Diana
Publikováno v:
Journal of Translational Medicine, Vol 10, Iss Suppl 3, p O3 (2012)
Externí odkaz:
https://doaj.org/article/942d87e0a5564e75a7fec04775251cb9
Autor:
Kuritcyn, Petr, Kletzander, Rosalie, Eisenberg, Sophia, Wittenberg, Thomas, Bruns, Volker, Evert, Katja, Keil, Felix, Ziegler, Paul K., Bankov, Katrin, Wild, Peter, Eckstein, Markus, Hartmann, Arndt, Geppert, Carol I., Benz, Michaela
Publikováno v:
In Journal of Pathology Informatics December 2024 15