452 citations · 560 across the 8 of their papers we have counts for
12 papers
Active learning using adaptable task-based prioritisation
Shaheer U. Saeed, João Ramalhinho, Mark Pinnock +9
Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
Image quality assessment for machine learning tasks using meta-reinforcement learning
Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides +8
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is per…
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
Yiwen Li, Yunguan Fu, Qianye Yang +6
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class a…
Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes
Yiwen Li, Gratianus Wesley Putra Data, Yunguan Fu +2
Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalis…
Adaptable image quality assessment using meta-reinforcement learning of task amenability
Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides +8
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjec…