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20192026
most citedA Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

805 citations · 838 across the 10 of their papers we have counts for

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Showing eess.IVShow all

5 papers · 1 filter

eess.IV2026

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…

eess.IV2024★ 1 cited

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…

eess.IV2021★ 20 cited

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…

eess.IV2020★ 10 cited

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…

eess.IV2019

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.…