204 citations · 323 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI
Patrick Godau, Akriti Srivastava, Constantin Ulrich +3
The field of medical imaging AI is currently undergoing rapid transformations, with methodical research increasingly translated into clinical practice. Despite these successes, res…
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…
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…
Quality Assured: Rethinking Annotation Strategies in Imaging AI
Tim Rädsch, Annika Reinke, Vivienn Weru +5
This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis:…
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
Lisa Mais, Peter Hirsch, Claire Managan +6
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morph…
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…