1 citations · 1 across the 1 of their papers we have counts for
5 papers
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +97
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…
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…
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…
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…