Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Buehler, Katja"'
Autor:
De Paolis, Gaia Romana, Lenis, Dimitrios, Novotny, Johannes, Wimmer, Maria, Berg, Astrid, Neubauer, Theresa, Winter, Philip Matthias, Major, David, Muthusami, Ariharasudhan, Schröcker, Gerald, Mienkina, Martin, Bühler, Katja
Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval and processing times not only potentially enhances swift response and decision-making in critical scenarios but also supports i
Externí odkaz:
http://arxiv.org/abs/2409.07100
Autor:
Winter, Philip Matthias, Wimmer, Maria, Major, David, Lenis, Dimitrios, Berg, Astrid, Neubauer, Theresa, De Paolis, Gaia Romana, Novotny, Johannes, Ulonska, Sophia, Bühler, Katja
This work addresses flexibility in deep learning by means of transductive reasoning. For adaptation to new data and tasks, e.g., in continual learning, existing methods typically involve tuning learnable parameters or complete re-training from scratc
Externí odkaz:
http://arxiv.org/abs/2403.11743
Autor:
Neubauer, Theresa, Berg, Astrid, Wimmer, Maria, Lenis, Dimitrios, Major, David, Winter, Philip Matthias, De Paolis, Gaia Romana, Novotny, Johannes, Lüftner, Daniel, Reinharter, Katja, Bühler, Katja
Quantitative measurement of crystals in high-resolution images allows for important insights into underlying material characteristics. Deep learning has shown great progress in vision-based automatic crystal size measurement, but current instance seg
Externí odkaz:
http://arxiv.org/abs/2401.03939
Autor:
Berg, Astrid, Vandersmissen, Eva, Wimmer, Maria, Major, David, Neubauer, Theresa, Lenis, Dimitrios, Cant, Jeroen, Snoeckx, Annemiek, Bühler, Katja
Publikováno v:
Computers in Biology and Medicine, Volume 154, 2023, 106543, ISSN 0010-4825
To facilitate both the detection and the interpretation of findings in chest X-rays, comparison with a previous image of the same patient is very valuable to radiologists. Today, the most common approach for deep learning methods to automatically ins
Externí odkaz:
http://arxiv.org/abs/2301.09338
Autor:
Dietrichstein, Marc, Major, David, Trapp, Martin, Wimmer, Maria, Lenis, Dimitrios, Winter, Philip, Berg, Astrid, Neubauer, Theresa, Bühler, Katja
Publikováno v:
LNCS 13609 (2022)
Unsupervised anomaly detection models which are trained solely by healthy data, have gained importance in the recent years, as the annotation of medical data is a tedious task. Autoencoders and generative adversarial networks are the standard anomaly
Externí odkaz:
http://arxiv.org/abs/2210.06188
Purpose: Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing. Methods: We propose a visualization approach to include ove
Externí odkaz:
http://arxiv.org/abs/2112.12590
Autor:
Wimmer, Maria, Sluiter, Gert, Major, David, Lenis, Dimitrios, Berg, Astrid, Neubauer, Theresa, Bühler, Katja
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., t
Externí odkaz:
http://arxiv.org/abs/2112.01320
Autor:
Neubauer, Theresa, Wimmer, Maria, Berg, Astrid, Major, David, Lenis, Dimitrios, Beyer, Thomas, Saponjski, Jelena, Bühler, Katja
Tumor segmentation in multimodal medical images has seen a growing trend towards deep learning based methods. Typically, studies dealing with this topic fuse multimodal image data to improve the tumor segmentation contour for a single imaging modalit
Externí odkaz:
http://arxiv.org/abs/2008.12544
The success of machine learning methods for computer vision tasks has driven a surge in computer assisted prediction for medicine and biology. Based on a data-driven relationship between input image and pathological classification, these predictors d
Externí odkaz:
http://arxiv.org/abs/2007.06312
Clinical applicability of automated decision support systems depends on a robust, well-understood classification interpretation. Artificial neural networks while achieving class-leading scores fall short in this regard. Therefore, numerous approaches
Externí odkaz:
http://arxiv.org/abs/2004.01610