Search Results for author:
Found 6 papers, 1 papers with code
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting.
Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies
In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols.
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Continual learning can adapt to a continuous data stream of a changing imaging environment.
Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance.
Unsupervised deep clustering for predictive texture pattern discovery in medical images
Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
