92 citations · 173 across the 12 of their papers we have counts for
12 papers
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…
CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis
Alexander Baumann, Leonardo Ayala, Silvia Seidlitz +5
Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote…
Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis
Jan Sellner, Alexander Studier-Fischer, Ahmad Bin Qasim +16
Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imag…
Deep intra-operative illumination calibration of hyperspectral cameras
Alexander Baumann, Leonardo Ayala, Alexander Studier-Fischer +6
Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. Currently available cameras, however, suffer from poor i…
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images
Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer +5
Robust semantic segmentation of intraoperative image data holds promise for enabling automatic surgical scene understanding and autonomous robotic surgery. While model development…
AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel +10
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Gi…
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg +11
Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulatio…
Semantic segmentation of surgical hyperspectral images under geometric domain shifts
Jan Sellner, Silvia Seidlitz, Alexander Studier-Fischer +5
Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, how…
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
Constantin Seibold, Simon Reiß, Saquib Sarfraz +7
In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner…
Robust deep learning-based semantic organ segmentation in hyperspectral images
Silvia Seidlitz, Jan Sellner, Jan Odenthal +11
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data a…
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
Maximilian Dietrich, Silvia Seidlitz, Nicholas Schreck +16
Sepsis is a leading cause of mortality and critical illness worldwide. While robust biomarkers for early diagnosis are still missing, recent work indicates that hyperspectral imagi…
Video-rate multispectral imaging in laparoscopic surgery: First-in-human application
Leonardo Ayala, Sebastian Wirkert, Anant Vemuri +6
Multispectral and hyperspectral imaging (MSI/HSI) can provide clinically relevant information on morphological and functional tissue properties. Application in the operating room (…