469 citations · 953 across the 22 of their papers we have counts for
23 papers
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +98
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
Anthropomorphic tissue-mimicking phantoms for oximetry validation in multispectral optical imaging
Kris Kristoffer Dreher, Janek Groehl, Friso Grace +11
Significance: Optical imaging of blood oxygenation (sO) can be achieved based on the differential absorption spectra of oxy- and deoxy-haemoglobin. A key challenge in realising…
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…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
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:…
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…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Self-distillation for surgical action recognition
Amine Yamlahi, Thuy Nuong Tran, Patrick Godau +9
Surgical scene understanding is a key prerequisite for contextaware decision support in the operating room. While deep learning-based approaches have already reached or even surpas…
Deployment of Image Analysis Algorithms under Prevalence Shifts
Patrick Godau, Piotr Kalinowski, Evangelia Christodoulou +5
Domain gaps are among the most relevant roadblocks in the clinical translation of machine learning (ML)-based solutions for medical image analysis. While current research focuses o…
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…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Sources of performance variability in deep learning-based polyp detection
Thuy Nuong Tran, Tim Adler, Amine Yamlahi +8
Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiativ…
Labeling instructions matter in biomedical image analysis
Tim Rädsch, Annika Reinke, Vivienn Weru +7
Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their op…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
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…
Semantic segmentation of multispectral photoacoustic images using deep learning
Melanie Schellenberg, Kris Dreher, Niklas Holzwarth +7
Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectr…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Photoacoustic image synthesis with generative adversarial networks
Melanie Schellenberg, Janek Gröhl, Kris K. Dreher +5
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the opt…
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern
Niklas Holzwarth, Melanie Schellenberg, Janek Gröhl +6
Purpose: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as the vessel topology and…
Surgical Data Science -- from Concepts toward Clinical Translation
Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya +47
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a…
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Lena Maier-Hein, Martin Wagner, Tobias Ross +30
Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracki…