6 papers
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…
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…
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…
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…
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Jeremias Traub, Till J. Bungert, Carsten T. Lüth +4
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenari…