5 citations · 11 across the 6 of their papers we have counts for
8 papers
Deformably-Scaled Transposed Convolution
Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu +3
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convo…
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…
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
Mou-Cheng Xu, Yu-Kun Zhou, Chen Jin +6
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consi…
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Yukun Zhou, Moucheng Xu, Yipeng Hu +5
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for bo…
Tissue characterization based on the analysis on i3DUS data for diagnosis support in neurosurgery
Mou-Cheng Xu
Brain shift makes the pre-operative MRI navigation highly inaccurate hence the intraoperative modalities are adopted in surgical theatre. Due to the excellent economic and portabil…
Learning To Pay Attention To Mistakes
Mou-Cheng Xu, Neil P. Oxtoby, Daniel C. Alexander +1
In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging…