Search Results for author: Yunzhi Huang

Found 10 papers, 7 papers with code

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

1 code implementation3 Jul 2023 Luyi Han, Tianyu Zhang, Yunzhi Huang, Haoran Dou, Xin Wang, Yuan Gao, Chunyao Lu, Tan Tao, Ritse Mann

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.

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

1 code implementation26 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.

Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI

1 code implementation1 Feb 2023 Luyi Han, Tao Tan, Tianyu Zhang, Yunzhi Huang, Xin Wang, Yuan Gao, Jonas Teuwen, Ritse Mann

Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.

Representation Learning

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

1 code implementation30 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.

Deformable Registration of Brain MR Images via a Hybrid Loss

no code implementations28 Oct 2021 Luyi Han, Haoran Dou, Yunzhi Huang, Pew-Thian Yap

Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields.

Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

1 code implementation14 Nov 2020 Jason St. John, Christian Herwig, Diana Kafkes, William A. Pellico, Gabriel N. Perdue, Andres Quintero-Parra, Brian A. Schupbach, Kiyomi Seiya, Nhan Tran, Javier M. Duarte, Yunzhi Huang, Malachi Schram, Rachael Keller

We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning.

Accelerator Physics

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