18 citations · 84 across the 15 of their papers we have counts for
7 papers · 1 filter
Neural Ordinary Differential Equation based Sequential Image Registration for Dynamic Characterization
Yifan Wu, Mengjin Dong, Rohit Jena +2
Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging.…
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation
Zeju Li, Konstantinos Kamnitsas, Qi Dou +2
This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta…
Semantic Concentration for Domain Adaptation
Shuang Li, Mixue Xie, Fangrui Lv +4
Domain adaptation (DA) paves the way for label annotation and dataset bias issues by the knowledge transfer from a label-rich source domain to a related but unlabeled target domain…
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
Chen Chen, Kerstin Hammernik, Cheng Ouyang +3
Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g. change of image appearances or contrasts caused by different…
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
Chen Qin, Bibo Shi, Rui Liao +3
We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal imag…
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
Wenjia Bai, Hideaki Suzuki, Chen Qin +4
Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct applic…