162 citations · 455 across the 51 of their papers we have counts for
9 papers · 1 filter
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…
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
Jack Breen, Kieran Zucker, Nicolas M. Orsi +1
Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number o…
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…
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…
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
Shuangchi He, Zehui Lin, Xin Yang +13
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical struc…
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…
A Deep Discontinuity-Preserving Image Registration Network
Xiang Chen, Nishant Ravikumar, Yan Xia +1
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions.…
CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning
Xiang Chen, Yan Xia, Nishant Ravikumar +1
Image registration is a fundamental building block for various applications in medical image analysis. To better explore the correlation between the fixed and moving images and imp…
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
Sulaiman Vesal, Mingxuan Gu, Ronak Kosti +2
Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despit…