Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Keicher, Matthias"'
In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists, which can have a detrimental effect on patients' healthcare. Large Language Models (LLMs) have the pot
Externí odkaz:
http://arxiv.org/abs/2409.06351
Autor:
Atad, Matan, Schinz, David, Moeller, Hendrik, Graf, Robert, Wiestler, Benedikt, Rueckert, Daniel, Navab, Nassir, Kirschke, Jan S., Keicher, Matthias
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE approaches require an additional model and are typica
Externí odkaz:
http://arxiv.org/abs/2408.01571
Every day, countless surgeries are performed worldwide, each within the distinct settings of operating rooms (ORs) that vary not only in their setups but also in the personnel, tools, and equipment used. This inherent diversity poses a substantial ch
Externí odkaz:
http://arxiv.org/abs/2404.07031
Radiology reporting is a crucial part of the communication between radiologists and other medical professionals, but it can be time-consuming and error-prone. One approach to alleviate this is structured reporting, which saves time and enables a more
Externí odkaz:
http://arxiv.org/abs/2307.05766
Autor:
Xiong, Yiheng, Liu, Jingsong, Zaripova, Kamilia, Sharifzadeh, Sahand, Keicher, Matthias, Navab, Nassir
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods. However, the dire
Externí odkaz:
http://arxiv.org/abs/2303.13818
Autor:
Pellegrini, Chantal, Keicher, Matthias, Özsoy, Ege, Jiraskova, Petra, Braren, Rickmer, Navab, Nassir
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making. However, such systems usually need to be trained on large amounts of annotated data, which often is scarce in the medical domain. Zero-shot
Externí odkaz:
http://arxiv.org/abs/2303.13391
Autor:
Keicher, Matthias, Atad, Matan, Schinz, David, Gersing, Alexandra S., Foreman, Sarah C., Goller, Sophia S., Weissinger, Juergen, Rischewski, Jon, Dietrich, Anna-Sophia, Wiestler, Benedikt, Kirschke, Jan S., Navab, Nassir
Vertebral fractures are a consequence of osteoporosis, with significant health implications for affected patients. Unfortunately, grading their severity using CT exams is hard and subjective, motivating automated grading methods. However, current app
Externí odkaz:
http://arxiv.org/abs/2303.12031
Autor:
Atad, Matan, Dmytrenko, Vitalii, Li, Yitong, Zhang, Xinyue, Keicher, Matthias, Kirschke, Jan, Wiestler, Bene, Khakzar, Ashkan, Navab, Nassir
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works predominantly focus on identifying the contribution of input features
Externí odkaz:
http://arxiv.org/abs/2207.07553
Autor:
Kollovieh, Marcel, Keicher, Matthias, Wunderlich, Stephan, Burwinkel, Hendrik, Wendler, Thomas, Navab, Nassir
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagn
Externí odkaz:
http://arxiv.org/abs/2206.08078
Autor:
Engstler, Paul, Keicher, Matthias, Schinz, David, Mach, Kristina, Gersing, Alexandra S., Foreman, Sarah C., Goller, Sophia S., Weissinger, Juergen, Rischewski, Jon, Dietrich, Anna-Sophia, Wiestler, Benedikt, Kirschke, Jan S., Khakzar, Ashkan, Navab, Nassir
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also leads to explainable and verbose findings that can assist the radiologist
Externí odkaz:
http://arxiv.org/abs/2203.16273