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20182023
most citedAutomatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning

41 citations · 87 across the 14 of their papers we have counts for

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Showing eess.IVShow all

7 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★ 2 cited

Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism

Tianyu Zhang, Luyi Han, Anna D'Angelo +7

Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides supe…

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.IV2023

IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data

Tianyu Zhang, Tao Tan, Luyi Han +5

Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of les…

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★ 41 cited

Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning

Fazael Ayatollahi, Shahriar B. Shokouhi, Ritse M. Mann +1

Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensiona…