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20202023
most citedAn Auto-Context Deformable Registration Network for Infant Brain MRI

7 citations · 11 across the 9 of their papers we have counts for

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6 papers · 1 filter

eess.IV2023★ 1 cited

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…

eess.IV2023

An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

Luyi Han, Tianyu Zhang, Yunzhi Huang +6

Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address th…

eess.IV2022

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network

Yunzhi Huang, Sahar Ahmad, Luyi Han +6

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans fr…

eess.IV2022

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)…

eess.IV2021★ 1 cited

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Alessa Hering, Lasse Hansen, Tony C. W. Mok +50

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…

eess.IV2021★ 1 cited

Deformable Registration of Brain MR Images via a Hybrid Loss

Luyi Han, Haoran Dou, Yunzhi Huang +1

Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields. These models typically depend on the i…