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.
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.
Separation of target anatomical structure and occlusions in chest radiographs
Chest radiographs are commonly performed low-cost exams for screening and diagnosis.
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms.
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging
We show that these semantic profiles enable higher recall and precision during retrieval compared to visual features, and that we can even map semantic terms describing clinical findings from radiology reports to localized image volume areas.
