452 citations · 794 across the 41 of their papers we have counts for
8 papers · 1 filter
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…
Meta-Learning Initializations for Interactive Medical Image Registration
Zachary M. C. Baum, Yipeng Hu, Dean Barratt
We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algo…
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Mou-Cheng Xu, Yukun Zhou, Chen Jin +5
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) al…
The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation
Wen Yan, Qianye Yang, Tom Syer +6
Dice similarity coefficient (DSC) and Hausdorff distance (HD) are widely used for evaluating medical image segmentation. They have also been criticised, when reported alone, for th…
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…