477 citations · 1.6k across the 72 of their papers we have counts for
74 papers
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
Bilal Sidiqi, Yipei Wang, Wen Yan +3
Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on global overlap loss functions te…
Continual pretraining Prostate MRI ViT Encoder for task alignment
Bilal A. Sidiqi, Yipei Wang, Shaheer U. Saeed +3
Building foundation models for MRI has emerged as a promising direction for advancing medical image analysis and improving accessibility of task specific models. Existing approache…
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
Abbas Al-Sabbagh, Shalom F. Mushtaq, Tomás M. da Silva +7
Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning algorithms exhibit complementa…
Active few-shot segmentation by reinforcing data selection
Chenlan Zhao, Benny Wong, Timothy F. Lundberg +8
Few-shot learning enables medical image segmentation models to adapt to new tasks using only a small number of labelled examples. However, adaptation performance depends strongly o…
Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
Yipei Wang, Shiqi Huang, Wen Yan +6
Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information…
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
Qi Li, Yuliang Huang, Shaheer U. Saeed +7
Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While probabilistic multi-rater appr…