Zobrazeno 1 - 10
of 7 178
pro vyhledávání: '"Lang A M"'
Autor:
Osuala, Richard, Joshi, Smriti, Tsirikoglou, Apostolia, Garrucho, Lidia, Pinaya, Walter H. L., Lang, Daniel M., Schnabel, Julia A., Diaz, Oliver, Lekadir, Karim
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition. Using a conditional generative adversarial network, we predict DCE-MRI
Externí odkaz:
http://arxiv.org/abs/2409.18872
Autor:
Wuttke, Alexander, Aßenmacher, Matthias, Klamm, Christopher, Lang, Max M., Würschinger, Quirin, Kreuter, Frauke
Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to express unanticipated thoughts in their own words, while conversatio
Externí odkaz:
http://arxiv.org/abs/2410.01824
Autor:
Kiechle, Johannes, Lang, Daniel M., Fischer, Stefan M., Felsner, Lina, Peeken, Jan C., Schnabel, Julia A.
Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to
Externí odkaz:
http://arxiv.org/abs/2407.17219
Denoising diffusion probabilistic models enable high-fidelity image synthesis and editing. In biomedicine, these models facilitate counterfactual image editing, producing pairs of images where one is edited to simulate hypothetical conditions. For ex
Externí odkaz:
http://arxiv.org/abs/2407.15270
Autor:
Osuala, Richard, Lang, Daniel M., Riess, Anneliese, Kaissis, Georgios, Szafranowska, Zuzanna, Skorupko, Grzegorz, Diaz, Oliver, Schnabel, Julia A., Lekadir, Karim
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and sharing of data commonly associated to patient privacy concerns. Such
Externí odkaz:
http://arxiv.org/abs/2407.12669
Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
Autor:
Fischer, Stefan M., Felsner, Lina, Osuala, Richard, Kiechle, Johannes, Lang, Daniel M., Peeken, Jan C., Schnabel, Julia A.
In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually i
Externí odkaz:
http://arxiv.org/abs/2407.07853
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the
Externí odkaz:
http://arxiv.org/abs/2406.14365
Autor:
Osuala, Richard, Lang, Daniel M., Verma, Preeti, Joshi, Smriti, Tsirikoglou, Apostolia, Skorupko, Grzegorz, Kushibar, Kaisar, Garrucho, Lidia, Pinaya, Walter H. L., Diaz, Oliver, Schnabel, Julia A., Lekadir, Karim
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent adm
Externí odkaz:
http://arxiv.org/abs/2403.13890
Multivariate imputation by chained equations (MICE) is one of the most popular approaches to address missing values in a data set. This approach requires specifying a univariate imputation model for every variable under imputation. The specification
Externí odkaz:
http://arxiv.org/abs/2309.01608
Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model is the masked autoencoder (MAE) which was developed on natural imaging
Externí odkaz:
http://arxiv.org/abs/2303.05861