Search

Your search keyword '"Gao, Yongfeng"' showing total 592 results
Start Over Author "Gao, Yongfeng"
592 results on '"Gao, Yongfeng"'

2. Proceedings of the 17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine

Author

Huang, Chuan, Vaska, Paul, Gao, Yongfeng, Chang, Shaojie, Holmes, Thomas Wesley, Pourmorteza, Amir, and Liang, Jerome

Subjects
Physics - Medical Physics
Abstract

Contained within this volume are the scholarly contributions presented in both oral and poster formats at Fully3D 2023: The 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. This conference convened from July 16-21, 2023, at Stony Brook University in New York. For ease of reference, all papers are organized alphabetically according to the last names of the primary authors. Our heartfelt appreciation goes out to all participants who took the time to submit, present, and revise their work for inclusion in these proceedings. Collectively, we would also like to express our profound gratitude to our generous sponsors, detailed in subsequent pages, who have played an instrumental role in offering awards and facilitating the various conference activities. Additionally, our thanks extend to the diligent reporter who collated invaluable feedback from attendees, which can be found in the pages that follow. September 7, 2023 Fully3D 2023 Co-Chairs: Jerome Liang, Paul Vaska, and Chuan Huang, Comment: arXiv admin note: contains arXiv:2302.03285, arXiv:2302.06251, arXiv:2303.11724, arXiv:2301.06604, arXiv:2302.06436, arXiv:2212.12519, arXiv:2304.08350, arXiv:2209.10350, arXiv:2310.04299, arXiv:2310.10694

Published
2023

3. Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity

Author

Cao, Weiguo, Pomeroy, Marc J., Liang, Zhengrong, Gao, Yongfeng, Shi, Yongyi, Tan, Jiaxing, Han, Fangfang, Wang, Jing, Ma, Jianhua, Lu, Hongbin, Abbasi, Almas F., and Pickhardt, Perry J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The elasticity of soft tissues has been widely considered as a characteristic property to differentiate between healthy and vicious tissues and, therefore, motivated several elasticity imaging modalities, such as Ultrasound Elastography, Magnetic Resonance Elastography, and Optical Coherence Elastography. This paper proposes an alternative approach of modeling the elasticity using Computed Tomography (CT) imaging modality for model-based feature extraction machine learning (ML) differentiation of lesions. The model describes a dynamic non-rigid (or elastic) deformation in differential manifold to mimic the soft tissues elasticity under wave fluctuation in vivo. Based on the model, three local deformation invariants are constructed by two tensors defined by the first and second order derivatives from the CT images and used to generate elastic feature maps after normalization via a novel signal suppression method. The model-based elastic image features are extracted from the feature maps and fed to machine learning to perform lesion classifications. Two pathologically proven image datasets of colon polyps (44 malignant and 43 benign) and lung nodules (46 malignant and 20 benign) were used to evaluate the proposed model-based lesion classification. The outcomes of this modeling approach reached the score of area under the curve of the receiver operating characteristics of 94.2 % for the polyps and 87.4 % for the nodules, resulting in an average gain of 5 % to 30 % over ten existing state-of-the-art lesion classification methods. The gains by modeling tissue elasticity for ML differentiation of lesions are striking, indicating the great potential of exploring the modeling strategy to other tissue properties for ML differentiation of lesions., Comment: 12 pages, 4 figures, 3 tables

Published
2022

17. A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset

Author

Zhang, Shu, Wu, Jinru, Yu, Sigang, Wang, Ruoyang, Shi, Enze, Gao, Yongfeng, Liang, Zhengrong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Xiang, editor, Lv, Jinglei, editor, Huo, Yuankai, editor, Dong, Bin, editor, Leahy, Richard M., editor, and Li, Quanzheng, editor

Published
2022
Full Text
View/download PDF

19. Evaluation of the novel endophytic fungus Chaetomium ascotrichoides 1‐24‐2 from Pinus massoniana as a biocontrol agent against pine wilt disease caused by Bursaphelenchus xylophilus

Author

Kamaruzzaman, Md, primary, Zheng, Lijun, additional, Zhou, Shun, additional, Ye, Wenhua, additional, Yuan, Yongqiang, additional, Qi, Qiu, additional, Gao, Yongfeng, additional, Tan, Jiajin, additional, Wang, Yan, additional, Chen, Bingjia, additional, Li, Zhiguang, additional, Liu, Songsong, additional, Mi, Renjun, additional, Zhang, Ke, additional, Zhao, Chen, additional, Ahmed, Waqar, additional, and Wang, Xinrong, additional

Published
2024
Full Text
View/download PDF

28. Electron Bunch Train Excited Higher-Order Modes in a Superconducting RF Cavity

Author

Gao, Yongfeng, Huang, Senlin, Wang, Fang, Feng, Liwen, Zhuang, Dehao, Lin, Lin, Zhu, Feng, Hao, Jiankui, Quan, Shengwen, and Liu, Kexin

Subjects
Physics - Accelerator Physics
Abstract

Higher-order mode (HOM) based intra-cavity beam diagnostics has been proved effectively and conveniently in superconducting radio-frequency (SRF) accelerators. Our recent research shows that the beam harmonics in the bunch train excited HOM spectrum, which have much higher signal-to-noise ratio than the intrinsic HOM peaks, may also be useful for beam diagnostics. In this paper, we will present our study on bunch train excited HOMs, including the theoretic model and recent experiments carried out based on the DC-SRF photoinjector and SRF linac at Peking University., Comment: Supported by National Natural Science Foundation of China (11275014)

Published
2016
Full Text
View/download PDF

32. Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction

Author

Shi, Yongyi, Gao, Yongfeng, Xu, Qiong, Li, Yang, Mou, Xuanqin, and Liang, Zhengrong

Abstract

Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image suffers photon count starving problem. To make full use of the correlation among different channel images to suppress the data noise and enhance the texture details in reconstructing each channel image, this paper proposes a tensor neural network (TNN) architecture to learn a multi-channel texture prior for PCCT reconstruction. Specifically, we first learn a spatial texture prior in each individual channel image by modeling the relationship between the center pixels and its corresponding neighbor pixels using a neural network. Then, we merge the single channel spatial texture prior into multi-channel neural network to learn the spectral local correlation information among different channel images. Since our proposed TNN is trained on a series of unpaired small spatial-spectral cubes which are extracted from one single reference multi-channel image, the local correlation in the spatial-spectral cubes is considered by TNN. To boost the TNN performance, a low-rank representation is also employed to consider the global correlation among different channel images. Finally, we integrate the learned TNN and the low-rank representation as priors into Bayesian reconstruction framework. To evaluate the performance of the proposed method, four references are considered. One is simulated images from ultra-high-resolution CT. One is spectral images from dual-energy CT. The other two are animal tissue and preclinical mouse images from a custom-made PCCT systems. Our TNN prior Bayesian reconstruction demonstrated better performance than other state-of-the-art competing algorithms, in terms of not only preserving texture feature but also suppressing image noise in each channel image.

Published
2024
Full Text
View/download PDF

35. ELONGATED HYPOCOTYL 5a modulates FLOWERING LOCUS T2 and gibberellin levels to control dormancy and bud break in poplar

Author

Gao, Yongfeng, primary, Chen, Zihao, additional, Feng, Qian, additional, Long, Tao, additional, Ding, Jihua, additional, Shu, Peng, additional, Deng, Heng, additional, Yu, Peizhi, additional, Tan, Wenrong, additional, Liu, Siqin, additional, Rodriguez, Lucas Gutierrez, additional, Wang, Lijun, additional, Resco de Dios, Víctor, additional, and Yao, Yinan, additional

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results