1 code implementation • 30 Nov 2023 • Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab, Matthias Keicher
Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.
1 code implementation • 11 Jul 2023 • Chantal Pellegrini, Matthias Keicher, Ege Özsoy, Nassir Navab
However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods.
Ranked #1 on Structured Report Generation on Rad-ReStruct
1 code implementation • 24 Mar 2023 • Yiheng Xiong, Jingsong Liu, Kamilia Zaripova, Sahand Sharifzadeh, Matthias Keicher, Nassir Navab
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.
1 code implementation • 23 Mar 2023 • Chantal Pellegrini, Matthias Keicher, Ege Özsoy, Petra Jiraskova, Rickmer Braren, Nassir Navab
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making.
no code implementations • 21 Mar 2023 • Matthias Keicher, Matan Atad, David Schinz, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Nassir Navab
We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale.
1 code implementation • 15 Jul 2022 • Matan Atad, Vitalii Dmytrenko, Yitong Li, Xinyue Zhang, Matthias Keicher, Jan Kirschke, Bene Wiestler, Ashkan Khakzar, Nassir Navab
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.
no code implementations • 16 Jun 2022 • Marcel Kollovieh, Matthias Keicher, Stephan Wunderlich, Hendrik Burwinkel, Thomas Wendler, Nassir Navab
To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD.
1 code implementation • 30 Mar 2022 • Paul Engstler, Matthias Keicher, David Schinz, Kristina Mach, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Ashkan Khakzar, Nassir Navab
Do black-box neural network models learn clinically relevant features for fracture diagnosis?
no code implementations • 29 Mar 2022 • Matthias Keicher, Kamilia Zaripova, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task.
no code implementations • 21 Mar 2022 • Tobias Czempiel, Coco Rogers, Matthias Keicher, Magdalini Paschali, Rickmer Braren, Egon Burian, Marcus Makowski, Nassir Navab, Thomas Wendler, Seong Tae Kim
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
no code implementations • 29 Jul 2021 • Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
no code implementations • 19 Mar 2021 • Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim, Nassir Navab
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.
1 code implementation • 12 Mar 2021 • Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.
no code implementations • 12 Aug 2020 • Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher, Josep Henry, Paul Thomson, Nassir Navab
In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset.
no code implementations • 2 May 2020 • Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi
Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.
2 code implementations • 24 Mar 2020 • Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems.
Ranked #4 on Surgical phase recognition on Cholec80