Hongyi Gu
5 Papers
Hongyi Gu is an academic researcher. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 2, co-authored 4 publications.
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Papers
β, β-Dimethylacrylshikonin potentiates paclitaxel activity, suppresses immune evasion and triple negative breast cancer progression via STAT3Y705 phosphorylation inhibition based on network pharmacology and transcriptomics analysis.
Zhixuan Wu,Haodong Wu,Ziqiong Wang,Huongfeng Li,Hongyi Gu,Erjie Xia,Congzhi Yan,Yinwei Dai,Conghui Liu,Xiaowu Wang,Linxi Lv,Jingxia Bao,Ouchen Wang,Xuanxuan Dai +13 more
TL;DR: In this paper , β, β-Dimethylacrylshikonin (DMAS), an active naphthoquinone derived from comfrey root, has potent anticancer activity.
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A Novel Nomogram Model to Identify Candidates and Predict the Possibility of Benefit From Primary Tumor Resection Among Female Patients With Metastatic Infiltrating Duct Carcinoma of the Breast: A Large Cohort Study
TL;DR: It is suggested that primary tumor surgery improved the prognosis of female patients with stage IV BIDC and a nomogram to quantify the probability of surgical benefit to help identify surgical candidates clinically was developed.
Accelerated MRI with Deep Linear Convolutional Transform Learning
Hongyi Gu,Burhaneddin Yaman,Steen Moeller,Il Yong Chun,Mehmet Akcakaya +4 more
- 17 Apr 2022
TL;DR: This work combines ideas from CS, TL and DL reconstructions to learn deep linear convolutional transforms as part of an algorithm unrolling approach and shows that the proposed technique can reconstruct MR images to a level comparable to DL methods, while supporting uniform undersampling patterns unlike conventional CS methods.
Revisiting ℓ1-wavelet compressed-sensing MRI in the era of deep learning
TL;DR: It is shown that ℓ1-wavelet CS can be fine-tuned to a level close to DL reconstruction for accelerated MRI by using ideas such as algorithm unrolling and advanced optimization methods over large databases that DL algorithms utilize.
Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI
Hongyi Gu,Chi Zhang,Zidan Yu,C. Rettenmeier,V. A. Stenger,Mehmet Akçakaya +5 more
TL;DR: This work proposes to use a physics-driven deep learning (PD-DL) reconstruction to accelerate multi-echo spiral fMRI by 10-fold and modify a self-supervised learning algorithm for optimized training with non-Cartesian trajectories and use it to train the PD-DL network.