805 citations · 838 across the 10 of their papers we have counts for
10 papers
Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis
Marc Rodríguez, Grzegorz Skorupko, Nay Aung +3
Synthetic image generation is a promising strategy to address data scarcity and the underrepresentation of clinically important phenotypes in medical imaging, yet generating images…
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Jieyun Bai, Yitong Tang, Zihao Zhou +36
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled dat…
Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport
A. Anil Sinaci, Senan Postaci, Dogukan Cavdaroglu +6
Objective: To develop the AI Product Passport, a standards-based framework improving transparency, traceability, and compliance in healthcare AI via lifecycle-based documentation.…
MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection
Marawan Elbatel, Anbang Wang, Keyuan Liu +6
This paper does not introduce a novel architecture; instead, it revisits a fundamental yet overlooked baseline: adapting human-centric foundation models for anatomical landmark det…
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
Jianmei Jiang, Huijin Wang, Jieyun Bai +4
The segmentation of the pubic symphysis and fetal head (PSFH) constitutes a pivotal step in monitoring labor progression and identifying potential delivery complications. Despite t…
Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models
Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska +6
While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased…
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
Ahmed Salih, Zahra Raisi-Estabragh, Ilaria Boscolo Galazzo +4
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communica…
Domain generalization in deep learning for contrast-enhanced imaging
Carla Sendra-Balcells, Víctor M. Campello, Carlos Martín-Isla +5
The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhance…
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Xiahai Zhuang, Jiahang Xu, Xinzhe Luo +20
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suff…
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Víctor M. Campello, Carlos Martín-Isla, Cristian Izquierdo +3
Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue.…