1.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Radboud University Medical CenterNL11 papers
- Radboud University NijmegenNL11 papers
- RWTH Aachen UniversityDE11 papers
- Charité - Universitätsmedizin BerlinDE7 papers
- University of BremenDE7 papers
- University of LübeckDE7 papers
- German Cancer Research CenterDE6 papers
- Heidelberg UniversityDE6 papers
- Brigham and Women's HospitalUS5 papers
- Harvard UniversityUS5 papers
- University Hospital HeidelbergDE5 papers
- Freie Universität BerlinDE4 papers
6 papers · 1 filter
meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis
Dishantkumar Sutariya, Eike Petersen
Analyzing machine learning model performance stratified by patient and recording properties is becoming the accepted norm and often yields crucial insights about important model fa…
From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction
Henning Höfener, Farina Kock, Martina Pontones +9
Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. Wh…
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Vincent Olesen, Nina Weng, Aasa Feragen +1
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical…
Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approach
Stephan Naunheim, Yannick Kuhl, David Schug +2
Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection…
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi +8
Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of…
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Tianyu Han, Sven Nebelung, Federico Pedersoli +8
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversaria…