Search Results for author:
Found 11 papers, 3 papers with code
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The field of automatic biomedical image analysis crucially depends on robust and meaningful performance metrics for algorithm validation.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner.
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
Sepsis is a leading cause of mortality and critical illness worldwide.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Image-based tracking of medical instruments is an integral part of surgical data science applications.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
Methods and open-source toolkit for analyzing and visualizing challenge results
Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms.
BIAS: Transparent reporting of biomedical image analysis challenges
The number of biomedical image analysis challenges organized per year is steadily increasing.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task.
