activity
20172022
most citedFed-Sim: Federated Simulation for Medical Imaging

6 citations · 20 across the 17 of their papers we have counts for

collaborators

22 papers

cs.CV20224 cited

Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images

Xiang Chen, Yan Xia, Nishant Ravikumar +1

Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recen…

eess.SP2022

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Jie Zhang, Yihui Zhao, Tianzhe Bao +5

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performan…

cs.CV2021

The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays

Rachael Harkness, Geoff Hall, Alejandro F Frangi +2

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community…

cs.CV20211 cited

Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction

Mingyuan Luo, Xin Yang, Xiaoqiong Huang +6

3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addres…

cs.CV2021

Flip Learning: Erase to Segment

Yuhao Huang, Xin Yang, Yuxin Zou +7

Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome…

eess.IV20211 cited

Style Curriculum Learning for Robust Medical Image Segmentation

Zhendong Liu, Van Manh, Xin Yang +6

The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced…