19 papers
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…
Reasoning in machine vision by learning fast and slow thinking
Shaheer U. Saeed, Yipei Wang, Veeru Kasivisvanathan +4
Reasoning is a hallmark of human intelligence, enabling adaptive decision-making in complex unfamiliar scenarios. In contrast, machine intelligence remains bound to training data,…
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…
Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction
YuCheng Tang, Wen Yan, Alexander Ng +13
Single-shot echo-planar prostate diffusion-weighted imaging (DWI) is frequently complicated by geometric distortions, which impact the ability to derive reliable diagnoses from suc…
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
Weixi Yi, Yipei Wang, Wen Yan +10
Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-we…