1 code implementation • 16 Apr 2024 • Deepa Krishnaswamy, Bálint Kovács, Stefan Denner, Steve Pieper, David Clunie, Christopher P. Bridge, Tina Kapur, Klaus H. Maier-Hein, Andrey Fedorov
With the wealth of medical image data, efficient curation is essential.
no code implementations • 19 Mar 2024 • Karol Gotkowski, Carsten Lüth, Paul F. Jäger, Sebastian Ziegler, Lars Krämer, Stefan Denner, Shuhan Xiao, Nico Disch, Klaus H. Maier-Hein, Fabian Isensee
We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.
1 code implementation • 11 Mar 2024 • Stefan Denner, David Zimmerer, Dimitrios Bounias, Markus Bujotzek, Shuhan Xiao, Lisa Kausch, Philipp Schader, Tobias Penzkofer, Paul F. Jäger, Klaus Maier-Hein
Despite these challenges, our research underscores the vast potential of foundation models for CBIR in radiology, proposing a shift towards versatile, general-purpose medical image retrieval systems that do not require specific tuning.
no code implementations • 29 Sep 2023 • Stefan Denner, Jonas Scherer, Klaus Kades, Dimitrios Bounias, Philipp Schader, Lisa Kausch, Markus Bujotzek, Andreas Michael Bucher, Tobias Penzkofer, Klaus Maier-Hein
In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy.
1 code implementation • 7 Apr 2020 • Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
1 code implementation • 7 Apr 2020 • Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab
Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation.