Zobrazeno 1 - 10
of 389
pro vyhledávání: '"Ingrisch, Michael"'
We address the computational barrier of deploying advanced deep learning segmentation models in clinical settings by studying the efficacy of network compression through tensor decomposition. We propose a post-training Tucker factorization that enabl
Externí odkaz:
http://arxiv.org/abs/2404.09683
Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Methods: An orthogonalization is utilized to remove the influence of protected features (e.g., age, sex, race) in CXR embeddings, ensurin
Externí odkaz:
http://arxiv.org/abs/2311.01349
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons. However, chest X-rays are acquired at a much higher resolution to display subtle pathologies. This study invest
Externí odkaz:
http://arxiv.org/abs/2306.06051
Chest X-ray (CXR) images are commonly compressed to a lower resolution and bit depth to reduce their size, potentially altering subtle diagnostic features. Radiologists use windowing operations to enhance image contrast, but the impact of such operat
Externí odkaz:
http://arxiv.org/abs/2306.06038
Autor:
Wollek, Alessandro, Haitzer, Philip, Sedlmeyr, Thomas, Hyska, Sardi, Rueckel, Johannes, Sabel, Bastian, Ingrisch, Michael, Lasser, Tobias
Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive and time-consuming manual lab
Externí odkaz:
http://arxiv.org/abs/2306.05997
Autor:
Wollek, Alessandro, Hyska, Sardi, Sedlmeyr, Thomas, Haitzer, Philip, Rueckel, Johannes, Sabel, Bastian O., Ingrisch, Michael, Lasser, Tobias
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports. An automatic label extraction model was designed based on the CheXpert architecture, and a web
Externí odkaz:
http://arxiv.org/abs/2306.02777
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image quality. A popular approach is to employ undersampling patterns followin
Externí odkaz:
http://arxiv.org/abs/2305.16376
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by p
Externí odkaz:
http://arxiv.org/abs/2303.11224
Autor:
Jeblick, Katharina, Schachtner, Balthasar, Dexl, Jakob, Mittermeier, Andreas, Stüber, Anna Theresa, Topalis, Johanna, Weber, Tobias, Wesp, Philipp, Sabel, Bastian, Ricke, Jens, Ingrisch, Michael
The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to appl
Externí odkaz:
http://arxiv.org/abs/2212.14882
To investigate the impact of OOD radiographs on existing chest X-ray classification models and to increase their robustness against OOD data. The study employed the commonly used chest X-ray classification model, CheXnet, trained on the chest X-ray 1
Externí odkaz:
http://arxiv.org/abs/2208.01077