22 citations · 38 across the 17 of their papers we have counts for
17 papers
Statistical Distance-Guided Unsupervised Domain Adaptation for Automated Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment
Shahabedin Nabavi, Kian Anvari Hamedani, Mohsen Ebrahimi Moghaddam +2
This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assess…
An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation
Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi +1
Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomi…
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
Fengming Lin, Yan Xia, Michael MacRaild +6
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotat…
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Fengming Lin, Yan Xia, Michael MacRaild +6
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cros…
Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
Nurbanu Aksoy, Nishant Ravikumar, Alejandro F Frangi
Medical image interpretation is central to most clinical applications such as disease diagnosis, treatment planning, and prognostication. In clinical practice, radiologists examine…
Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation
Nurbanu Aksoy, Serge Sharoff, Selcuk Baser +2
Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the im…