2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation
Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung +3
Deep learning models for medical image segmentation often struggle when deployed across different datasets due to domain shifts - variations in both image appearance, known as styl…
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…
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 +4
This paper addresses the task of cross-modal medical image segmentation by exploring unsupervised domain adaptation (UDA) approaches. We propose a model-agnostic UDA framework, Low…
RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans
Mark C. Eid, Pak-Hei Yeung, Madeleine K. Wyburd +2
Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D…