1 code implementation • 17 Mar 2024 • Shahabedin Nabavi, Kian Anvari Hamedani, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi
Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification.
no code implementations • 10 Mar 2024 • Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi, Ali Gooya
Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes.
1 code implementation • 23 Feb 2024 • Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
1 code implementation • 23 Feb 2024 • Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Nina Cheng, Nishant Ravikumar, Alejandro F. Frangi
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning.
no code implementations • 22 Nov 2023 • Nicolás Gaggion, Benjamin A. Matheson, Yan Xia, Rodrigo Bonazzola, Nishant Ravikumar, Zeike A. Taylor, Diego H. Milone, Alejandro F. Frangi, Enzo Ferrante
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.
no code implementations • 24 Aug 2023 • Yash Deo, Rodrigo Bonazzola, Haoran Dou, Yan Xia, Tianyou Wei, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila
We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.
no code implementations • 13 Aug 2023 • Yash Deo, Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain.
no code implementations • 26 Jun 2023 • 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.
1 code implementation • 26 Jun 2023 • Haoran Dou, Ning Bi, Luyi Han, Yuhao Huang, Ritse Mann, Xin Yang, Dong Ni, Nishant Ravikumar, Alejandro F. Frangi, Yunzhi Huang
In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses.
1 code implementation • 23 Mar 2023 • Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi
Methods: The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks.
no code implementations • 22 Nov 2022 • Jie Zhang, Yihui Zhao, Tianzhe Bao, Zhenhong Li, Kun Qian, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang
The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model.
no code implementations • 4 Oct 2022 • Haoran Dou, Seppo Virtanen, Nishant Ravikumar, Alejandro F. Frangi
Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz.
no code implementations • 22 Aug 2022 • Mojtaba Lashgari, Nishant Ravikumar, Irvin Teh, Jing-Rebecca Li, David L. Buckley, Jurgen E. Schneider, Alejandro F. Frangi
We extend previous studies accounting for the cardiomyocyte shape variability, water exchange between the cardiomyocytes (intercalated discs), myocardial microstructure disarray, and four sheetlet orientations.
no code implementations • 4 Jul 2022 • Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Shengquan Xie, Zhiqiang Zhang
At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint.
no code implementations • 1 Jul 2022 • Yuxin Zou, Haoran Dou, Yuhao Huang, Xin Yang, Jikuan Qian, Chaojiong Zhen, Xiaodan Ji, Nishant Ravikumar, Guoqiang Chen, Weijun Huang, Alejandro F. Frangi, Dong Ni
First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space.
1 code implementation • 30 Jun 2022 • Haoran Dou, Luyi Han, Yushuang He, Jun Xu, Nishant Ravikumar, Ritse Mann, Alejandro F. Frangi, Pew-Thian Yap, Yunzhi Huang
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
no code implementations • 14 Jun 2022 • Shahabedin Nabavi, Mohammad Hashemi, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi
The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96. 25\% and 94. 51\%, respectively, which increases to 96. 88\% and 95. 72\% after improving using the proposed salient region detection model.
no code implementations • 13 Dec 2021 • Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam, Alejandro F. Frangi, Ahmad Ali Abin
Increasing the speed of training and testing can be achieved with the proposed model in the frequency domain.
no code implementations • 6 Aug 2021 • Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Kelsey L. Pomykala, Jens Kleesiek, Alejandro F. Frangi, Jan Egger
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).
1 code implementation • 7 Jul 2021 • Mohammad Hamghalam, Alejandro F. Frangi, Baiying Lei, Amber L. Simpson
In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e. g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations.
no code implementations • 1 Oct 2020 • Shahabedin Nabavi, Azar Ejmalian, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi, Mohammad Mohammadi, Hamidreza Saligheh Rad
The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them.
no code implementations • 1 Sep 2020 • Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F. Frangi, Sanja Fidler
Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.
no code implementations • 6 Jul 2020 • Francisco J. Ibarrola, Nishant Ravikumar, Alejandro F. Frangi
With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.
no code implementations • 2 Jul 2019 • Rahman Attar, Marco Pereanez, Christopher Bowles, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles.
no code implementations • 10 Jan 2019 • Rahman Attar, Marco Pereanez, Ali Gooya, Xenia Alba, Le Zhang, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset.
no code implementations • 6 Nov 2018 • Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment.
no code implementations • 6 Jun 2018 • Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider
International challenges have become the standard for validation of biomedical image analysis methods.
no code implementations • 15 Jun 2017 • Yawen Huang, Ling Shao, Alejandro F. Frangi
Cross-modal image synthesis is a topical problem in medical image computing.
no code implementations • CVPR 2017 • Yawen Huang, Ling Shao, Alejandro F. Frangi
We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis.
no code implementations • Pattern Recognition 2003 • Jian Yang; Zhong Jin; Jing-yu Yang, David Zhang, Alejandro F. Frangi
In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i. e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA).