387 citations · 645 across the 41 of their papers we have counts for
42 papers
ReportMedSAM: Guiding Segmentation Through Radiology Reports
Anghong Du, Theodoros N. Arvanitis, Colin Watts +2
Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural languag…
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…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Learned Local Attention Maps for Synthesising Vessel Segmentations
Yash Deo, Rodrigo Bonazzola, Haoran Dou +5
Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse ev…
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
Yash Deo, Haoran Dou, Nishant Ravikumar +2
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of…
Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels
Fengming Lin, Yan Xia, Nishant Ravikumar +3
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment. However, existing methods face challenges in capturing small vessels and hand…
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices. Typically, the generated VP should capture sufficient variab…
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
Haoran Dou, Ning Bi, Luyi Han +7
Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objecti…
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images
Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam +2
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Suffi…
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
Rachael Harkness, Alejandro F Frangi, Kieran Zucker +1
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis i…
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar +5
Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic…
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…
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…
Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI
Mojtaba Lashgari, Nishant Ravikumar, Irvin Teh +4
In silico tissue models enable evaluating quantitative models of magnetic resonance imaging. This includes validating and sensitivity analysis of imaging biomarkers and tissue micr…
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG
Jie Zhang, Yihui Zhao, Fergus Shone +4
Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (…
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound
Yuxin Zou, Haoran Dou, Yuhao Huang +9
Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide compl…
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Haoran Dou, Luyi Han, Yushuang He +6
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US)…
Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model
Shahabedin Nabavi, Mohammad Hashemi, Mohsen Ebrahimi Moghaddam +2
Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic qua…
Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains
Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam +2
Population imaging studies rely upon good quality medical imagery before downstream image quantification. This study provides an automated approach to assess image quality from car…
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…
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…
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
Yuan Jin, Antonio Pepe, Jianning Li +6
The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissecti…