204 citations · 323 across the 12 of their papers we have counts for
12 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…
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…
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:…
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…
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…
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…