19 citations · 43 across the 14 of their papers we have counts for
6 papers · 1 filter
FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning
Yubin Zheng, Pak-Hei Yeung, Jing Xia +4
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without exposing local data, balancing performance and privacy. However, domain sh…
Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model
Pak-Hei Yeung, Jayroop Ramesh, Pengfei Lyu +2
This paper explores the transfer of knowledge from general vision models pretrained on 2D natural images to improve 3D medical image segmentation. We focus on the semi-supervised s…
Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse
Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of traumatic brain injuries and neurodegene…
Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images
Jayroop Ramesh, Valentin Bacher, Mark C. Eid +6
The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal he…
Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation
Pengfei Lyu, Pak-Hei Yeung, Xiaosheng Yu +5
This paper addresses cross-modal medical image segmentation, focusing on MRI-CT transfer in a source-only domain generalization setting. During training, only source-modality sampl…
Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
Joshua Omolegan, Pak Hei Yeung, Madeleine K. Wyburd +5
Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions…