Zhang Guanglei, is an associate professor at the School of Biological Science and Medical Engineering, Beijing University of Aeronautics and Astronautics, a researcher at the Biomedical Engineering High-End Innovation Center, and a doctoral supervisor. From 2010 to 2014, he pursued his Ph.D. in Biomedical Engineering at the School of Medicine, Tsinghua University. From 2015 to 2016, he conducted postdoctoral research at the School of Medicine, Stanford University. From 2017 to 2018, he served as an associate professor and deputy director at the Medical Intelligence Institute of Beijing Jiaotong University. In 2018, he joined the School of Biological Science and Medical Engineering at Beihang University, where he was approved for the university's "Medical Engineering Hundred Talents Program" and the "Young Top Talent Program." He currently holds memberships in IEEE, the Chinese Society of Biomedical Engineering, the Biomedical Photonics Branch, the Young Committee of the Medical Physics Branch, the Chinese Society of Image and Graphics, and is a senior member of the Chinese Society of Optical Engineering.

The "Intelligent Medical Laboratory" was established under the auspices of the Biomedical Engineering High-End Innovation Center at Beijing University of Aeronautics and Astronautics, with researcher Zhang Guanglei serving as the head. The research directions of the "Intelligent Medical Laboratory" include: three-dimensional optical molecular imaging technology, artificial intelligence analysis methods for medical imaging, and medical wearable devices. The laboratory's scientific achievements include: over ninety research papers published in journals such as IEEE TMI, IEEE TNNLS, IEEE TBME, IEEE TCI, IEEE TIM, IEEE TAI, IEEE JBHI, OL, BOE, MP, PMB, Nano Energy, and ACS Nano; more than twenty national invention patent applications, one approved software copyright for pathological image AI analysis, and one "China Patent Excellence Award"; over twenty funded projects, including national key research and development programs, National Natural Science Foundation of China projects, and Beijing Natural Science Foundation projects; the development of a multimodal super-resolution fluorescence molecular three-dimensional imaging system capable of near-infrared I and II multi-spectral fluorescence molecular imaging, which breaks through the spatial resolution limitations of traditional imaging systems; and the development of medical wearable devices such as portable smart ECG cards, cuffless photoelectric blood pressure monitors, non-invasive blood glucose meters, and multi-parameter intelligent human body monitors.

 

Representative Papers (Biomedical Imaging)： 

 

[1]     Z. Liu, T. Zhang, Y. He, B. Wen, H. Guo, P. Zhang, C. Ma, S. Lyu, Y. Feng, Y. Zhao, Y. Jin, D. Zhao, and G. Zhang*, “Cellflow: Advancing pathological image augmentation from spatial views to temporal trajectories,” Med. Image Anal., 2026, 110, 103995. (SCI, Q1, IF=11.8)

[2]     Z. Liu, Y. He, T. Zhang, C. Ma, F. Song, H. Wu, R. Cai, H. Guo, H. Zhang, B. Wen, P. Zhang, D. Zhao, and G. Zhang*, “StainExpert: A unified multi-expert diffusion framework for multi-target pathological stain translation,” IEEE Trans. Med. Imag., 2025, in press. (SCI, Q1, IF=9.8)

[3]     T. Zhang, Z. Yan, C. Li, N. Ying, S. Lyu, Y. Feng, Y. Zhao, and G. Zhang*, “CellMix: A general instance relationship-based method for data augmentation toward pathology image classification,” IEEE Trans. Neural Netw. Learn. Syst., 2025, 36(9): 16020–16034. (SCI, IF=10.2)

[4]     Y. He, Z. Liu, M. Qi, S. Ding, P. Zhang, F. Song, C. Ma, H. Wu, R. Cai, Y. Feng, H. Zhang, T. Zhang, and G. Zhang*, “PST-Diff: Achieving high-consistency stain transfer by diffusion models with pathological and structural constraints,” IEEE Trans. Med. Imag., 2024, 43(10): 3634–3647. (SCI, IF=10.6)

[5]     X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y. Sun, Y. Feng, and G. Zhang*, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Trans. Med. Imag., 2022, 41(12): 3812–3823. (SCI, IF=10.6)

[6]     X. Zhang, X. Cao, P. Zhang, F. Song, J. Zhang, L. Zhang, and G. Zhang*, “Self-training strategy based on finite element method for adaptive bioluminescence tomography reconstruction,” IEEE Trans. Med. Imag., 2022, 41(10): 2629–2643. (SCI, IF=10.6)

[7]     P. Zhang, G. Fan, T. Xing, F. Song, and G. Zhang*, “UHR-DeepFMT: Ultra-high spatial resolution reconstruction of fluorescence molecular tomography based on 3D fusion dual-sampling deep neural network,” IEEE Trans. Med. Imag., 2021, 40(11): 3217–3228. (SCI, IF=10.6)

[8]     G. Zhang*, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam X-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imag., 2017, 36(1): 225–235. (SCI, IF=10.6)

[9]     G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Bayesian framework based direct reconstruction of fluorescence parametric images,” IEEE Trans. Med. Imag., 2015, 34(6): 1378–1391. (SCI, IF=10.6)

[10]  H. Wu, Y. He, Z. Liu, P. Zhang, F. Song, C. Ma, R. Cai, and G. Zhang*, “Non-invasive anatomical level cerebrovascular imaging of mice using diffusion model-enhanced fluorescence imaging,” Laser Photon. Rev., 2025, 19(7): 2401193. (SCI, IF=9.8)

  

Representative Papers (Biomedical Signal)： 

 

[1]     C. Ma, L. Guo, H. Zhang, Z. Liu, G. Zhang*, “DiffCNBP: Lightweight diffusion model for IoMT-based continuous cuffless blood pressure waveform monitoring using PPG,” IEEE Internet of Things Journal, 2025, 12(1): 61–80. (SCI, Q1, IF=8.9)

[2]     C. Ma, Z. Liu, P. Zhang, L. Guo, H. Zhang, Z. Liu, G. Zhang*, “Mutual transfer learning for cuff-less blood pressure estimation using photoplethysmography-based visibility graphs,” Eng. Appl. Artif. Intel., 2025, 161, 122099. (SCI, Q1, IF=8.0)

[3]     W. Yan, C. Ma, X. Cai, Y. Sun, G. Zhang*, and W. Song*, “Self-powered and wireless physiological monitoring system with integrated power supply and sensors,” Nano Energy, 2023, 108, 108203. (SCI, IF=17.1)

[4]     C. Ma, P. Zhang, F. Song, Z. Liu, Y. Feng, Y. He, and G. Zhang*, “UPR-BP: Unsupervised Photoplethysmography Representation Learning for Noninvasive Blood Pressure Estimation,” IEEE Trans. Artif. Intell., 2024, 5(9): 4696–4707.

[5]     C. Ma, P. Zhang, H. Zhang, Z. Liu, F. Song, Y. He, and G. Zhang*, “STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography,” Expert Syst. Appl., 2024, 249: 123809. (SCI, IF=8.5)

[6]     C. Ma, Y. Sun, P. Zhang, F. Song, Y. Feng, Y. He, and G. Zhang*, “SMART-BP: Sem-Resnet and auto-regressor based on a two-stage framework for noninvasive blood pressure measurement,” IEEE Trans. Instrum. Meas., 2024, 73: 2503718. (SCI, IF=5.6)

[7]     C. Ma, Y. Xu, P. Zhang, F. Song, Y. Sun, Y. Feng, Y. He, G. Zhang*, “PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model,” IEEE J. Biomed. Health Inform., 2025, 29 (6): 3918-3929. (SCI, IF=7.7)

[8]     C. Ma, P. Zhang, F. Song, Y. Sun, G. Fan, T. Zhang, Y. Feng, and G. Zhang*, “KD-Informer: cuff-less continuous blood pressure waveform estimation approach based on single photoplethysmography,” IEEE J. Biomed. Health Inform., 2023, 27(5): 2219–2230. (SCI, IF=7.7)

[9]     P. Zhang, C. Ma, Y. Sun, G. Fan, F. Song, Y. Feng, and G. Zhang*, “Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings,” Comput. Biol. Med., 2021, 139: 104880. (SCI, IF=7.7)

[10]  W. Cai, Y. Chen, J. Guo, B. Han, Y.Shi, L. Ji, J. Wang, G. Zhang*, and J. Luo, “Accurate detection of atrial fibrillation from 12-Lead ECG using deep neural network,” Comput. Biol. Med., 2020, 116: 103378. (SCI, IF=7.7)

Research Papers: 

     

[1]     Z. Liu, T. Zhang, Y. He, B. Wen, H. Guo, P. Zhang, C. Ma, S. Lyu, Y. Feng, Y. Zhao, Y. Jin, D. Zhao, and G. Zhang*, “Cellflow: Advancing pathological image augmentation from spatial views to temporal trajectories,” Med. Image Anal., 2026, 110, 103995. (SCI, Q1, IF=11.8)

[2]     Z. Liu, Y. He, T. Zhang, C. Ma, F. Song, H. Wu, R. Cai, H. Guo, H. Zhang, B. Wen, P. Zhang, D. Zhao, and G. Zhang*, “StainExpert: A unified multi-expert diffusion framework for multi-target pathological stain translation,” IEEE Trans. Med. Imag., 2025, in press. (SCI, Q1, IF=9.8)

[3]     Z. Liu, B. Wen, T. Zhang, P. Zhang, Y. He, C. Ma, H. Guo, N. Ying, S. Lyu, and G. Zhang*, “OptiPathD: A capacity-optimized diffusion foundation model for pathology image generation,” IEEE BIBM, 2025, in press.

[4]     H. Zhang, C. Ma, and G. Zhang*, “MDFSBP: A multi-perspective differential feature space framework for estimating blood pressure using photoplethysmography (PPG),” Med. Nov. Technol. Devices, 2025, 28, 100407. (SCI, 封面文章)

[5]     C. Ma, Z. Liu, P. Zhang, L. Guo, H. Zhang, Z. Liu, and G. Zhang*, “Mutual transfer learning for cuff-less blood pressure estimation using photoplethysmography-based visibility graphs,” Eng. Appl. Artif. Intel., 2025, 161, 122099. (SCI, Q1, IF=8.0)

[6]     N. Ying, Y. Lei, T. Zhang, S. Lyu, S. Chen, Z. Liu, Y. Feng, Y. Zhao, and G. Zhang*, “CPIA dataset: A large-scale comprehensive pathological image analysis dataset for self-supervised learning pre-training,” Biomed. Signal Process. Control, 2025, 110, 108148. (SCI, Q2, IF=4.9)

[7]     T. Zhang, Z. Yan, C. Li, N. Ying, S. Lyu, Y. Feng, Y. Zhao, and G. Zhang*, “CellMix: A general instance relationship-based method for data augmentation toward pathology image classification,” IEEE Trans. Neural Netw. Learn. Syst., 2025, 36(9): 16020–16034. (SCI, Q1, IF=10.2)

[8]     P. Zhang, X. Liu, Q. Xue, Y. Shang, H. Gao, J. Liang, W. Wang, and G. Zhang*, “PAH2T-Former: Paired-attention hybrid hierarchical transformer for synergistically enhanced FMT reconstruction quality and efficiency,” IEEE Trans. Comput. Imag., 2025, 11: 536–545. (SCI, Q1, IF=4.2)

[9]     F. Song, P. Zhang, H. Wu, C. Ma, Z. Liu, R. Cai, Y. Feng, Y. He, X. Dong, Y. Tian*, and G. Zhang*, “Advances of fluorescence molecular imaging: NIR-II window, probes and tomography,” Laser Photon. Rev., 2025, 19(10): 2400275. (SCI, Q1, IF=9.8)

[10]  W. Yan, H. Zhang, X. Cai, C. Ma, D. Ma, H. Lu*, G. Zhang*, W. Song*, “Multi-channel wearable fiber sensors with high sensitivity for limb motion recognition,” J. Mater. Chem. A, 2025, 13(6): 4503–4512. (SCI, Q1, IF=10.8)

[11]  H. Wu, Y. He, Z. Liu, P. Zhang, F. Song, C. Ma, R. Cai, and G. Zhang*, “Non-invasive anatomical level cerebrovascular imaging of mice using diffusion model-enhanced fluorescence imaging,” Laser Photon. Rev., 2025, 19(7): 2401193. (SCI, Q1, IF=9.8)

[12]  C. Ma, L. Guo, H. Zhang, Z. Liu, G. Zhang*, “DiffCNBP: Lightweight diffusion model for IoMT-based continuous cuffless blood pressure waveform monitoring using PPG,” IEEE Internet Things J., 2025, 12(1): 61–80. (SCI, Q1, IF=8.2)

[13]  X. Zhang, X. Cao, J. Zhang, L. Zhang, and G. Zhang*, “Neural-field-based image reconstruction for bioluminescence tomography,” J. Innov. Opt. Heal. Sci., 2025, 18(1): 2550002. (SCI, Q2, IF=2.3)

[14]  C. Ma, Y. Xu, P. Zhang, F. Song, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model,” IEEE J. Biomed. Health Inform., 2025, 29 (6): 3918–3929. (SCI, Q1, IF=7.7) (ESI高被引论文)

[15]  Y. He, Z. Liu, M. Qi, S. Ding, P. Zhang, F. Song, C. Ma, H. Wu, R. Cai, Y. Feng, H. Zhang, T. Zhang, and G. Zhang*, “PST-Diff: Achieving high-consistency stain transfer by diffusion models with pathological and structural constraints,” IEEE Trans. Med. Imag., 2024, 43(10): 3634–3647. (SCI, Q1, IF=8.9)

[16]  Z. Liu, T. Zhang, Y. He, and G. Zhang*, “Generating progressive images from pathological transitions via diffusion model,” MICCAI, 2024, 15011: 308-318.

[17]  C. Ma, P. Zhang, F. Song, Z. Liu, Y. Feng, Y. He, and G. Zhang*, “UPR-BP: Unsupervised photoplethysmography representation learning for noninvasive blood pressure estimation,” IEEE Trans. Artif. Intell., 2024, 5(9): 4696–4707. (SCI)

[18]  C. Ma, P. Zhang, H. Zhang, Z. Liu, F. Song, Y. He, and G. Zhang*, “STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography,” Expert Syst. Appl., 2024, 249: 123809. (SCI, Q1, IF=8.5)

[19]  Q. Shi, F. Song, X. Zhou, X. Chen, J. Cao, J. Na, Y. Fan*, G. Zhang*, L. Zheng*, “Early predicting osteogenic differentiation of mesenchymal stem cells based on deep learning within one day,” Ann. Biomed. Eng., 2024, 52 (6): 1706–1718. (SCI, Q1, IF=3.8)

[20]  F. Song, J. Tian, P. Zhang, C. Ma, Y. Sun, Y. Feng, T. Zhang, Y. Lei, Y. He, Z. Cai, Y. Cheng, and G. Zhang*, “A novel feature engineering method based on latent representation learning for radiomics: Application in NSCLC subtype classification,” IEEE J. Biomed. Health Inform., 2024, 28(1): 31–41. (SCI, Q1, IF=7.7)

[21]  C. Ma, Y. Sun, P. Zhang, F. Song, Y. Feng, Y. He, and G. Zhang*, “SMART-BP: Sem-Resnet and auto-regressor based on a two-stage framework for noninvasive blood pressure measurement,” IEEE Trans. Instrum. Meas., 2024, 73: 2503718. (SCI, Q1, IF=5.6)

[22]  T. Zhang, Y. Feng, Y. Zhao, Y. Lei, N. Ying, F. Song, Y. He, Z. Yan, Y. Feng, A. Yang, and G. Zhang*, “SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification,” Comput. Meth. Programs Biomed., 2024, 244: 107969. (SCI, Q1, IF=6.1)

[23]  P. Zhang, C. Ma, F. Song, Z. Liu, H. Wu, Y. Feng, Y. He, D. Wang, G. Zhang*, “SVRNet: First investigation of single-view reconstruction network for fluorescence molecular tomography,” IEEE Trans. Comput. Imag., 2023, 9: 834–845. (SCI, Q1, IF=5.4)

[24]  X. Zhang, Y. Jia, J. Cui, J. Zhang, X. Cao, L. Zhang, G. Zhang*, “Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction,” J. Opt. Soc. Am. A, 2023, 40(7): 1359–1371. (SCI, Q3, IF=1.9)

[25]  Y. He, F. Song, W. Wu, S. Tian, T. Zhang, S. Zhang, P. Zhang, C. Ma, Y. Feng, R. Yang, G. Zhang*, “MultiTrans: Multi-scale feature fusion Transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images,” Med. Nov. Technol. Devices, 2023, 18: 100235.

[26]  G. Zhang*, X. Ma, W. Qin, M. Jia, M. Chen, “Editorial: Optical Imaging in Neuroscience and Brain Disease,” Front. Neurosci., 2023, 17: 1192863. (SCI, Q2, IF=4.3)

[27]  Y. He, P. Xu, H. Wu, Y. Chu, and G. Zhang*, “The model of electrified cell clusters in biological tissues basing on the resting potential difference,” Med. Nov. Technol. Devices, 2023, 18: 100281.

[28]  W. Yan, C. Ma, X. Cai, Y. Sun, G. Zhang*, and W. Song*, “Self-powered and wireless physiological monitoring system with integrated power supply and sensors,” Nano Energy, 2023, 108, 108203. (SCI, Q1, IF=17.6)

[29]  P. Zhang, F. Song, C. Ma, Z. Liu, H. Wu, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy,” Phys. Med. Biol., 2023, 68: 04LT01. (SCI, Q2, IF=3.5)

[30]  P. Zhang, C. Ma, F. Song, Y. Sun, Y. Feng, Y. He, T. Zhang, and G. Zhang*, “D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection,” Biomed. Signal Process. Control, 2023, 82, 104615. (SCI, Q2, IF=5.1)

[31]  F. Song, X. Song, Y. Feng, G. Fan, Y. Sun, P. Zhang, J. Li, F. Liu, and G. Zhang*, “Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer: a multi-dataset study,” Med. Phys., 2023, 50(7): 4351–4365. (SCI, Q2, IF=3.8)

[32]  T. Zhang, Y. Feng, Y. Zhao, G. Fan, A. Yang, S. Lyu, P. Zhang, F. Song, C. Ma, Y. Sun, Y. Feng, and G. Zhang*, “MSHT: Multi-stage hybrid transformer for the ROSE image analysis of pancreatic cancer,” IEEE J. Biomed. Health Inform., 2023, 27(4): 1946–1957. (SCI, Q1, IF=7.7) (Featured Article)

[33]  X. Zhang, J. Cui, Y. Jia, P. Zhang, F. Song, X. Cao, J. Zhang, L. Zhang, G. Zhang*, “Image restoration for blurry optical images caused by photon diffusion with deep learning,” J. Opt. Soc. Am. A, 2023, 40(1): 96–107. (SCI, Q3, IF=2.104)

[34]  P. Zhang, C. Ma, F. Song, T. Zhang, Y. Sun, Y. Feng, Y. He, F. Liu, D. Wang, and G. Zhang*, “D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain,” Comput. Meth. Programs Biomed., 2023, 229, 107293. (SCI, Q1, IF=7.027)

[35]  C. Ma, P. Zhang, F. Song, Y. Sun, G. Fan, T. Zhang, Y. Feng, and G. Zhang*, “KD-Informer: Cuff-less continuous blood pressure waveform estimation approach based on single photoplethysmography,” IEEE J. Biomed. Health Inform., 2023, 27(5): 2219–2230. (SCI, Q1, IF=7.021)

[36]  H. Li, Y. Liu, X. Liang, Y. Yuan, Y. Cheng, G. Zhang, S. Tamura, “Multi-object tracking via deep feature fusion and association analysis,” Eng. Appl. Artif. Intel., 2023, 124: 106527. (SCI, Q1, IF=8.0)

[37]  X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y. Sun, Y. Feng, and G. Zhang*, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Trans. Med. Imag., 2022, 41(12): 3812–3823. (SCI, Q1, IF=11.037)

[38]  P. Zhang, C. Ma, F. Song, Z. Liu, Y. Feng, Y. Sun, Y. He, F. Liu, D. Wang, and G. Zhang*, “Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography,” Biomed. Opt. Express, 2022, 13(10): 5327–5343. (SCI, Q2, IF=3.562)

[39]  F. Liu, P. Zhang, Z. Liu, F. Song, C. Ma, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography,” J. Biomed. Opt., 2022, 27(7): 070501. (SCI, Q2, IF=3.582)

[40]  麻琛彬, 张鹏, 宋凡, 孙洋洋, 张光磊*, “基于光电容积脉搏波的无袖带血压测量技术研究进展,” 北京生物医学工程, 2023, 42(2): 194–203.

[41]  X. Zhang, X. Cao, P. Zhang, F. Song, J. Zhang, L. Zhang, and G. Zhang*, “Self-training strategy based on finite element method for adaptive bioluminescence tomography reconstruction,” IEEE Trans. Med. Imag., 2022, 41(10): 2629–2643. (SCI, Q1, IF=11.037)

[42]  J. Li, F. Song, P. Zhang, C. Ma, T. Zhang, Y. Sun, Y. Feng, X. Song, S. Lyu, and G. Zhang*, “A multi-classification model for non-small cell lung cancer subtypes based on independent subtask learning,” Med. Phys., 2022, 49: 6969–6974. (SCI, Q2, IF=4.506)

[43]  P. Zhang, C. Ma, F. Song, G. Fan, Y. Sun, Y. Feng, X. Ma, F. Liu, and G. Zhang*, “A review of advances in imaging methodology in fluorescence molecular tomography,” Phys. Med. Biol., 2022, 67: 10TR01. (SCI, Q2, IF=4.174)

[44]  Y. Feng, F. Song, P. Zhang, G. Fan, T. Zhang, X. Zhao, C. Ma, Y. Sun, X. Song, H. Pu, F. Liu, and G. Zhang*, “Prediction of EGFR mutation status in non-small cell lung cancer based on ensemble learning,” Front. Pharmaco., 2022,13: 897597. (SCI, Q1, IF=5.988)

[45]  F. Song, L. Song, T. Xing, X. Song, P. Zhang, Y. Feng, Z. Zhu, W. Song, and G. Zhang*, “A multi-classification model for predicting the invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules,” Front. Oncol., 2022, 12: 800811. (SCI, Q2, IF=5.738)

[46]  G. Zhang*, X. Chen, S. Wang, J. Li, and X. Cao, “Editorial: Optical Molecular Imaging in Cancer Research,” Front. Oncol., 2022, 12: 870583. (SCI, Q2, IF=5.738)

[47]  W. Zhao, G. Zhang, J. Li, “Accuracy improvement of demodulating the stress field with StressUnet in photoelasticity,” Appl. Opt., 2022, 61(29): 8678–8687. (SCI, Q3, IF=1.905)

[48]  P. Zhang, F. Song, C. Ma, Z. Liu, and G. Zhang*, “Multi-attention prior based residual encoder-decoder network for fast and accurate reconstruction in fluorescence molecular tomography,” Proc. SPIE, 2022, 12506: 125063F.

[49]  P. Zhang, G. Fan, T. Xing, F. Song, and G. Zhang*, “UHR-DeepFMT: Ultra-high spatial resolution reconstruction of fluorescence molecular tomography based on 3D fusion dual-sampling deep neural network,” IEEE Trans. Med. Imag., 2021, 40(11): 3217–3228. (SCI, Q1, IF=11.037)

[50]  P. Zhang, C. Ma, Y. Sun, G. Fan, F. Song, Y. Feng, and G. Zhang*, “Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings,” Comput. Biol. Med., 2021, 139: 104880. (SCI, Q1, IF=6.698)

[51]  X. Zhao, P. Zhang, F. Song, G. Fan, Y. Sun, Y. Wang, Z. Tian, L. Zhang, and G. Zhang*, “D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution,” Comput. Biol. Med., 2021, 135: 104526. (SCI, Q1, IF=6.698)

[52]  Y. Gao, F. Song, P. Zhang, J. Liu, J. Cui, Y. Ma, G. Zhang*, and J. Luo, “Improving the subtype classification of non-small cell lung cancer by elastic deformation based machine learning,” J. Digit. Imaging, 2021, 34: 605–617. (SCI, Q2, IF=4.903)

[53]  L. Song, T. Xing, Z. Zhu, W. Han, G. Fan, J. Li, H. Du, W. Song, Z. Jin, and G. Zhang, “Hybrid clinical-radiomics model for precisely predicting the invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule,” Acad. Radiol., 2021, 28(9): e267–e277. (SCI, Q1, IF=5.482)

[54]  R. Liu, Z. Cai, Q. Zhang, H. Yuan, G. Zhang, and D. Yang, “Colorimetric two-dimensional photonic crystal biosensors for label-free detection of hydrogen peroxide,” Sens. Actuators B., 2021, 354:131236. (SCI, Q1, IF=9.221)

[55]  R. Zhao, D. Wu, J. Wen, Q. Zhang, G. Zhang, and J. Li, “Robustness and accuracy improvement of data processing with 2D neural networks for transient absorption dynamics,” Phys. Chem. Chem. Phys., 2021, 23: 16998-17008. (SCI, Q1, IF=3.945)

[56]  W. Cai, Y. Chen, J. Guo, B. Han, Y.Shi, L. Ji, J. Wang, G. Zhang*, and J. Luo, “Accurate detection of atrial fibrillation from 12-Lead ECG using deep neural network,” Comput. Biol. Med., 2020, 116: 103378. (SCI, Q1, IF=4.589)

[57]  Y. Yuan, W. Qin, B. Ibragimov, G. Zhang, B. Han, M. Q.-H. Meng, L. Xing, “Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition,” IEEE Trans. Autom. Sci. Eng., 2020, 17(2): 574–583. (SCI, Q1, IF=5.083)

[58]  Y. Li, Y. Liu, M. Zhang, G. Zhang, Z. Wang, and J. Luo, “Radiomics with attribute bagging for breast tumor classification using multimodal ultrasound images,” J. Ultras. Med., 2020, 39(2): 361–371. (SCI, Q2, IF=2.153)

[59]  L. Guo, F. Liu, C. Cai, J. Liu, and G. Zhang*, “3D deep encoder-decoder network for fluorescence molecular tomography,” Opt. Lett., 2019, 44(8): 1892–1895. (SCI, Q1, IF=3.776)

[60]  J. Liu, J. Cui, F. Liu, Y. Yuan, F. Guo, and G. Zhang*, “Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model,” Med. Phys., 2019, 46(7): 3091–3100. (SCI, Q1, IF=4.071)

[61]  L. Zhang, and G. Zhang*, “Brief review on learning based methods for optical tomography,” J. Innov. Opt. Heal. Sci., 2019, 12(6): 1930011. (SCI, Q3, IF=1.661)

[62]  S. Jiang, J. Liu, G. Zhang, Y. An, H. Meng, Y. Gao, K. Wang, and J. Tian, “Reconstruction of fluorescence molecular tomography via a fused LASSO method based on group sparsity prior,” IEEE Trans. Biomed. Eng., 2019, 66(5): 1361–1371. (SCI, Q1, IF=4.424)

[63]  Y. Liu, S. Jiang, J. Liu, Y. An, G. Zhang, Y. Gao, K. Wang, and J. Tian, “Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient,” J. Biomed. Opt., 2018, 23(8):085002. (SCI, Q2, IF=2.785)

[64]  G. Zhang*, S. Tzoumas, K. Cheng, F. Liu, J. Liu, J. Luo, J. Bai, and L. Xing, “Generalized adaptive Gaussian Markov random field for X-ray luminescence computed tomography,” IEEE Trans. Biomed. Eng., 2018, 65(9): 2130–2133. (SCI, Q1, IF=4.424)

[65]  K. Cheng, M. Sano, C. H. Jenkins, G. Zhang, D. Vernekohl, W. Zhao, C. Wei, Y. Zhang, Z. Zhang, Y. Liu, Z. Cheng, and L. Xing, “Synergistically enhancing the therapeutic effect of radiation therapy with radiation activatable and reactive oxygen species-releasing nanostructures,” ACS Nano, 2018, 12: 4946−4958. (SCI, Q1, IF=14.588)

[66]  K. Cheng, H. Chen, C. H. Jenkins, G. Zhang, W. Zhao, Z. Zhang, F. Han, J. Fung, M. Yang, Y. Jiang, L. Xing, and Z. Cheng, “Synthesis, characterization, and biomedical applications of a targeted dual-modal near-infrared-II fluorescence and photoacoustic imaging nanoprobe,” ACS Nano, 2017, 11:12276–12291. (SCI, Q1, IF=14.588)

[67]  G. Zhang*, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam X-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imag., 2017, 36(1): 225–235. (SCI, Q1, IF=6.685)

[68]  G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Bayesian framework based direct reconstruction of fluorescence parametric images,” IEEE Trans. Med. Imag., 2015, 34(6): 1378–1391. (SCI, Q1, IF=6.685)

[69]  G. Zhang, W. He, H. Pu, F. Liu, M. Chen, J. Bai and J. Luo, “Acceleration of dynamic fluorescence molecular tomography with principal component analysis,” Biomed. Opt. Express, 2015, 6(6): 2036–2055. (SCI, Q1, IF=3.921)

[70]  G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography,” Appl. Phys. Lett., 2015, 106(8): 081110. (SCI, Q1, IF=3.597)

[71]  G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A direct method with structural priors for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2014, 61(3): 986–990. (SCI, Q1, IF=4.424)

[72]  G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt., 2013, 18(4): 040505. (SCI, Q2, IF=2.785)

[73]  G. Zhang, X. Cao, B. Zhang, F. Liu, J. Luo, and J. Bai, “MAP estimation with structural priors for fluorescence molecular tomography,” Phys. Med. Biol., 2013, 58(2): 351–372. (SCI, Q2, IF=2.883)

[74]  W. He^#, G. Zhang^#, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt., 2014, 19(5): 056012. (SCI, Q2, IF=2.785, co-first author)

[75]  W. He^#, G. Zhang^#, F. Liu, X. Cao, J. Luo, and J. Bai, “Projected restarted framework for tomographic reconstruction,” Proc. of SPIE, 2014, 9230: 92300F. (EI, co-first author)

[76]  Y. An, J. Liu, G. Zhang, S. Jiang, J. Ye, C. Chi, and J. Tian, “Compactly supported radial basis function-based meshless method for photon propagation model of fluorescence molecular tomography,” IEEE Trans. Med. Imag., 2017, 36(2): 366–373. (SCI, Q1, IF=6.685)

[77]  Y. Liu, J. Liu, Y. An, S. Jiang, J. Ye, Y. Mao, K. He, G. Zhang, C. Chi, J. Tian, “Novel trace norm regularization method for fluorescence molecular tomography reconstruction,” Proc. of SPIE, 2017, 10047: 100470U. (EI)

[78]  S. Jiang, J. Liu, Y. An, G. Zhang, J. Ye, Y. Mao, K. He, C. Chi, and J. Tian, “Novel L2,1-norm optimization method for fluorescence molecular tomography reconstruction,” Biomed. Opt. Express, 2016, 7(6):2342–2359. (SCI, Q1, IF=3.921)

[79]  Y. An, J. Liu, G. Zhang, J. Ye, Y. Mao, S. Jiang, W. Shang, Y. Du, C. Chi, and J. Tian, “Meshless reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function,” J. Biomed. Opt., 2015, 20(10):105003. (SCI, Q2, IF=2.785)

[80]  Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, C. Chi, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2015, 62(7): 1818–1826. (SCI, Q1, IF=4.424)

[81]  X. Zhang, F. Liu, S. Zuo, J. Shi, G. Zhang, J. Bai, and J. Luo, “Reconstruction of fluorophore concentration variation in dynamic fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2015, 62(1): 138–144. (SCI, Q1, IF=4.424)

[82]  H. Pu, G. Zhang, W. He, F. Liu, H. Guang, Y. Zhang, J. Bai, and J. Luo, “Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis,” Phys. Med. Biol., 2014, 59(17): 5025–5042. (SCI, Q2, IF=2.883)

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1. 论文列表(全)

[1]     Tianyi Zhang, Shangqing Lyu, Yanli Lei, Yufang He, Chunhui Li, Nan Ying, Sicheng Chen, Yu Zhao, Yunlu Feng, and Guanglei Zhang*, “PuzzleTuning: Explicitly bridge pathological and natural image with puzzles,” CVPR, 2023.

 

 

[1]     Zeyu Liu, Tianyi Zhang, Yufang He, Bo Wen, Haoran Guo, Peng Zhang, Chenbin Ma, Shangqing Lyu, Yunlu Feng, Yu Zhao, Yueming Jin, Dachun Zhao, and Guanglei Zhang*, “Cellflow: Advancing pathological image augmentation from spatial views to temporal trajectories,” Medical Image Analysis, 2026, 110, 103995. (SCI, Q1, IF=11.8)

[2]     Zeyu Liu, Yufang He, Tianyi Zhang, Chenbin Ma, Fan Song, Huijie Wu, Ruxin Cai, Haoran Guo, Haonan Zhang, Bo Wen, Peng Zhang, Dachun Zhao, and Guanglei Zhang*, “StainExpert: A unified multi-expert diffusion framework for multi-target pathological stain translation,” IEEE Transactions on Medical Imaging, 2025, in press. (SCI, Q1, IF=9.8)

[3]     Zeyu Liu, Bo Wen, Tianyi Zhang, Peng Zhang, Yufang He, Chenbin Ma, Haoran Guo, Nan Ying, Shangqing Lyu, Guanglei Zhang, and Guanglei Zhang*, “OptiPathD: A capacity-optimized diffusion foundation model for pathology image generation,” IEEE BIBM, 2025, in press.

[4]     Haonan Zhang, Chenbin Ma, Guanglei Zhang*, “MDFSBP: A multi-perspective differential feature space framework for estimating blood pressure using photoplethysmography (PPG),” Medicine in Novel Technology and Devices, 2025, 28, 100407. (SCI)

[5]     Chenbin Ma, Zhenchang Liu, Peng Zhang, Lishuang Guo, Haonan Zhang, Zeyu Liu, Guanglei Zhang*, “Mutual transfer learning for cuff-less blood pressure estimation using photoplethysmography-based visibility graphs,” Engineering Applications of Artificial Intelligence, 2025, 161, 122099. (SCI, Q1, IF=8.0)

[6]     Nan Ying, Yanli Lei, Tianyi Zhang, Shangqing Lyu, Sicheng Chen, Zeyu Liu, Yunlu Feng, Yu Zhao, and Guanglei Zhang*, “CPIA dataset: A large-scale comprehensive pathological image analysis dataset for self-supervised learning pre-training,” Biomedical Signal Processing and Control, 2025, 110, 108148. (SCI, Q2, IF=4.9)

[7]     Tianyi Zhang, Zhiling Yan, Chunhui Li, Nan Ying, Shangqing Lyu, Yunlu Feng, Yu Zhao, and Guanglei Zhang*, “CellMix: A general instance relationship-based method for data augmentation toward pathology image classification,” IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(9): 16020–16034. (SCI, Q1, IF=10.2)

[8]     Peng Zhang, Xingyu Liu, Qianqian Xue, Yu Shang, Honglei Gao, Jiye Liang, Wenjian Wang and Guanglei Zhang*, “PAH2T-Former: Paired-attention hybrid hierarchical transformer for synergistically enhanced FMT reconstruction quality and efficiency,” IEEE Transactions on Computational Imaging, 2025, 11: 536–545. (SCI, Q1, IF=4.2)

[9]     Fan Song, Peng Zhang, Huijie Wu, Chenbin Ma, Zeyu Liu, Ruxin Cai, Youdan Feng, Yufang He, Xiaoman Dong, Ye Tian*, and Guanglei Zhang*, “Advances of fluorescence molecular imaging: NIR-II window, probes and tomography,” Laser & Photonics Reviews, 2025, 19(10): 2400275. (SCI, Q1, IF=9.8)

[10]  Wei Yan, Haonan Zhang, Xinxin Cai, Chenbin Ma, Dongmin Ma, Hongbo Lu, Guanglei Zhang*, Weixing Song*, “Multi-channel wearable fiber sensors with high sensitivity for limb motion recognition,” Journal Of Materials Chemistry A, 2025, 13(6): 4503–4512. (SCI, Q1, IF=10.8)

[11]  Huijie Wu, Yufang He, Zeyu Liu, Peng Zhang, Fan Song, Chenbin Ma, Ruxin Cai, and Guanglei Zhang*, “Non-invasive anatomical level cerebrovascular imaging of mice using diffusion model-enhanced fluorescence imaging,” Laser & Photonics Reviews, 2025, 19(7): 2401193. (SCI, Q1, IF=9.8)

[12]  Chenbin Ma, Lishuang Guo, Haonan Zhang, Zhenchang Liu, Guanglei Zhang*, “DiffCNBP: Lightweight diffusion model for IoMT-based continuous cuffless blood pressure waveform monitoring using PPG,” IEEE Internet of Things Journal, 2025, 12(1): 61–80. (SCI, Q1, IF=8.2)

[13]  Xuanxuan Zhang, Xu Cao, Jiulou Zhang, Lin Zhang, Guanglei Zhang*, “Neural-field-based image reconstruction for bioluminescence tomography,” Journal Of Innovative Optical Health Sciences, 2025, 18(1): 2550002. (SCI, Q2, IF=2.3)

[14]  Chenbin Ma, Yangfan Xu, Peng Zhang, Fan Song, Yangyang Sun, Youdan Feng, Yufang He, Guanglei Zhang*, “PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model,” IEEE Journal of Biomedical and Health Informatics, 2025, 29 (6): 3918–3929. (SCI, Q1, IF=7.7) (ESI高被引论文)

[15]  Yufang He, Zeyu Liu, Mingxin Qi, Shengwei Ding, Peng Zhang, Fan Song, Chenbin Ma, Huijie Wu, Ruxin Cai, Youdan Feng, Haonan Zhang, Tianyi Zhang, and Guanglei Zhang*, “PST-Diff: Achieving high-consistency stain transfer by diffusion models with pathological and structural constraints,” IEEE Transactions on Medical Imaging, 2024, 43(10): 3634–3647. (SCI, Q1, IF=8.9)

[16]  Zeyu Liu, Tianyi Zhang, Yufang He, and Guanglei Zhang*, “Generating progressive images from pathological transitions via diffusion model,” MICCAI, 2024, 15011: 308-318.

[17]  Chenbin Ma, Peng Zhang, Fan Song, Zeyu Liu, Youdan Feng, Yufang He, Guanglei Zhang*, “UPR-BP: Unsupervised photoplethysmography representation learning for noninvasive blood pressure estimation,” IEEE Transactions on Artificial Intelligence, 2024, 5(9): 4696–4707. (SCI)

[18]  Chenbin Ma, Peng Zhang, Haonan Zhang, Zeyu Liu, Fan Song, Yufang He, Guanglei Zhang*, “STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography,” Expert Systems With Applications, 2024, 249: 123809. (SCI, Q1, IF=8.5)

[19]  Qiusheng Shi, Fan Song, Xiaocheng Zhou, Xinyuan Chen, Jingqi Cao, Jing Na, Yubo Fan*, Guanglei Zhang*, Lisha Zheng*, “Early predicting osteogenic differentiation of mesenchymal stem cells based on deep learning within one day,” Annals of Biomedical Engineering, 2024, 52 (6): 1706–1718. (SCI, Q1, IF=3.8)

[20]  Fan Song, Jiaxin Tian, Peng Zhang, Chenbin Ma, Yangyang Sun,Youdan Feng, Tianyi Zhang, Yanli Lei, Yufang He, Zhongyu Cai, Yuanzhi Cheng, Guanglei Zhang*, “A novel feature engineering method based on latent representation learning for radiomics: Application in NSCLC subtype classification,” IEEE Journal of Biomedical and Health Informatics, 2024, 28(1): 31–41. (SCI, Q1, IF=7.7)

[21]  Chenbin Ma, Yangyang Sun, Peng Zhang, Fan Song, Youdan Feng, Yufang He, and Guanglei Zhang*, “SMART-BP: Sem-Resnet and auto-regressor based on a two-stage framework for noninvasive blood pressure measurement,” IEEE Transactions on Instrumentation and Measurement, 2024, 73: 2503718. (SCI, Q1, IF=5.6)

[22]  Tianyi Zhang, Youdan Feng, Yu Zhao, Yanli Lei, Nan Ying, Fan Song, Yufang He, Zhiling Yan, Yunlu Feng, Aiming Yang, and Guanglei Zhang*, “SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification,” Computer Methods and Programs in Biomedicine, 2024, 244: 107969. (SCI, Q1, IF=6.1)

[23]  Peng Zhang, Chenbin Ma, Fan Song, Zeyu Liu, Huijie Wu, Youdan Feng, Yufang He, Daifa Wang, Guanglei Zhang*, “SVRNet: First investigation of single-view reconstruction network for fluorescence molecular tomography,” IEEE Transactions on Computational Imaging, 2023, 9: 834–845. (SCI, Q1, IF=5.4)

[24]  Xuanxuan Zhang, Yunfei Jia, Jiapei Cui, Jiulou Zhang, Xu Cao, Lin Zhang, Guanglei Zhang*, “Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction,” Journal of the Optical Society of America A, 2023, 40(7): 1359–1371. (SCI, Q3, IF=1.9)

[25]  Yufang He, Fan Song, Wangjiang Wu, Suqing Tian, Tianyi Zhang, Shuming Zhang, Peng Zhang, Chenbin Ma, Youdan Feng, Ruijie Yang, Guanglei Zhang*, “MultiTrans: Multi-scale feature fusion Transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images,” Medicine in Novel Technology and Devices, 2023, 18: 100235.

[26]  Guanglei Zhang*, Xibo Ma, Wenjian Qin, Mengyu Jia, Maomao Chen, “Editorial: Optical Imaging in Neuroscience and Brain Disease,” Frontiers in Neuroscience, 2023, 17: 1192863. (SCI, Q2, IF=4.3)

[27]  Yufang He, Peiguo Xu, Huijie Wu, Yong Chu, Guanglei Zhang*, “The model of electrified cell clusters in biological tissues basing on the resting potential difference,” Medicine in Novel Technology and Devices, 2023, 18: 100281.

[28]  Wei Yan, Chenbin Ma, Xinxin Cai, Yangyang Sun, Guanglei Zhang*, Weixing Song*, “Self-powered and wireless physiological monitoring system with integrated power supply and sensors,” Nano Energy, 2023, 108, 108203. (SCI, Q1, IF=17.6)

[29]  Peng Zhang, Fan Song, Chenbin Ma, Zeyu Liu, Huijie Wu, Yangyang Sun, Youdan Feng, Yufang He, Guanglei Zhang*, “Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy,” Physics in Medicine & Biology, 2023, 68: 04LT01. (SCI, Q2, IF=3.5)

[30]  Peng Zhang, Chenbin Ma, Fan Song, Yangyang Sun, Youdan Feng, Yufang He, Tianyi Zhang, and Guanglei Zhang*, “D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection,” Biomedical Signal Processing and Control, 2023, 82, 104615. (SCI, Q2, IF=5.1)

[31]  Fan Song, Xiao Song, Youdan Feng, Guangda Fan, Yangyang Sun, Peng Zhang, Jiankai Li, Fei Liu, and Guanglei Zhang*, “Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer: a multi-dataset study,” Medical Physics, 2023, 50(7): 4351–4365. (SCI, Q2, IF=3.8)

[32]  Tianyi Zhang, Yunlu Feng, Yu Zhao, Guangda Fan, Aiming Yang, Shangqing Lyu, Peng Zhang, Fan Song, Chenbin Ma, Yangyang Sun, Youdan Feng, and Guanglei Zhang*, “MSHT: Multi-stage hybrid transformer for the ROSE image analysis of pancreatic cancer,” IEEE Journal of Biomedical and Health Informatics, 2023, 27(4): 1946–1957. (SCI, Q1, IF=7.7)

[33]  Xuanxuan Zhang, Jiapei Cui, Yunfei Jia, Peng Zhang, Fan Song, Xu Cao, Jiulou Zhang, Lin Zhang, Guanglei Zhang*, “Image restoration for blurry optical images caused by photon diffusion with deep learning,” Journal of the Optical Society of America A, 2023, 40(1): 96–107. (SCI, Q3, IF=1.9)

[34]  Peng Zhang, Chenbin Ma, Fan Song, Tianyi Zhang, Yangyang Sun, Youdan Feng, Yufang He, Fei Liu, Daifa Wang, Guanglei Zhang*, “D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain,” Computer Methods and Programs in Biomedicine, 2023, 229, 107293. (SCI, Q1, IF=6.1)

[35]  Chenbin Ma, Peng Zhang, Fan Song, Yangyang Sun, Guangda Fan, Tianyi Zhang, Youdan Feng, Guanglei Zhang*, “KD-Informer: Cuff-less continuous blood pressure waveform estimation approach based on single photoplethysmography,” IEEE Journal of Biomedical and Health Informatics, 2023, 27(5): 2219–2230. (SCI, Q1, IF=7.7)

[36]  Hui Li, Yapeng Liu, Xiaoguo Liang, Yongfeng Yuan, Yuanzhi Cheng, Guanglei Zhang, Shinichi Tamura, “Multi-object tracking via deep feature fusion and association analysis,” Engineering Applications of Artificial Intelligence, 2023, 124: 106527. (SCI, Q1, IF=8.0)

[37]  Xiangyu Zhao, Peng Zhang, Fan Song, Chenbin Ma, Guangda Fan, Yangyang Sun, Youdan Feng, Guanglei Zhang*, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Transactions on Medical Imaging, 2022, 41(12): 3812–3823. (SCI, Q1, IF=11.037)

[38]  Peng Zhang, Chenbin Ma, Fan Song, Zeyu Liu, Youdan Feng, Yangyang Sun, Yufang He, Fei Liu, Daifa Wang, Guanglei Zhang*, “Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography,” Biomedical Optics Express, 2022, 13(10): 5327–5343. (SCI, Q2, IF=3.4)

[39]  Fei Liu, Peng Zhang, Zeyu Liu, Fan Song, Chenbin Ma, Yangyang Sun, Youdan Feng, Yufang He, Guanglei Zhang*, “In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography,” Journal of Biomedical Optics, 2022, 27(7): 070501. (SCI, Q2, IF=3.5)

[40]  麻琛彬, 张鹏, 宋凡, 孙洋洋, 张光磊*, “基于光电容积脉搏波的无袖带血压测量技术研究进展,” 北京生物医学工程, 2023, 42(2): 194–203.

[41]  Xuanxuan Zhang, Xu Cao, Peng Zhang, Fan Song, Jiulou Zhang, Lin Zhang, Guanglei Zhang*, “Self-training strategy based on finite element method for adaptive bioluminescence tomography reconstruction,” IEEE Transactions on Medical Imaging, 2022, 41(10): 2629–2643. (SCI, Q1, IF=10.6)

[42]  Jinkai Li, Fan Song, Peng Zhang, Chenbin Ma, Tianyi Zhang, Yangyang Sun, Youdan Feng, Xiao Song, Shangqing Lyu, Guanglei Zhang*, “A multi-classification model for non-small cell lung cancer subtypes based on independent subtask learning,” Medical Physics, 2022, 49: 6969–6974. (SCI, Q2, IF=3.8)

[43]  Peng Zhang, Chenbin Ma, Fan Song, Guangda Fan, Yangyang Sun, Youdan Feng, Xibo Ma, Fei Liu, Guanglei Zhang*, “A review of imaging methodology advances in fluorescence molecular tomography,” Physics in Medicine & Biology, 2022, 67: 10TR01. (SCI, Q2, IF=3.5)

[44]  Youdan Feng, Fan Song, Peng Zhang, Guangda Fan, Tianyi Zhang, Xiangyu Zhao, Chenbin Ma, Yangyang Sun, Xiao Song, Huangsheng Pu, Fei Liu, Guanglei Zhang*, “Prediction of EGFR mutation status in non-small cell lung cancer based on ensemble learning,” Frontiers in Pharmacology, 2022, 13: 897597. (SCI, Q1, IF=5.6)

[45]  Fan Song, Lan Song, Tongtong Xing, Xiao Song, Peng Zhang, Youdan Feng, Zhenchen Zhu, Wei Song, Guanglei Zhang*, “A multi-classification model for predicting the invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules,” Frontiers in Oncology, 2022, 12: 800811. (SCI, Q2, IF=4.7)

[46]  Guanglei Zhang*, Xueli Chen, Shouju Wang, Jiao Li, Xu Cao, “Editorial: Optical Molecular Imaging in Cancer Research,” Frontiers in Oncology, 2022, 12: 870583. (SCI, Q2, IF=4.7)

[47]  Weiliang Zhao, Guanglei Zhang, Jiebo Li, “Accuracy improvement of demodulating the stress field with StressUnet in photoelasticity,” Applied Optics, 2022, 61(29): 8678–8687. (SCI, Q3, IF=1.9)

[48]  Peng Zhang, Fan Song, Chenbin Ma, Zeyu Liu, Guanglei Zhang*, “Multi-attention prior based residual encoder-decoder network for fast and accurate reconstruction in fluorescence molecular tomography,” Proc. SPIE, 2022, 12506: 125063F.

[49]  Peng Zhang, Guangda Fan, Tongtong Xing, Fan Song, Guanglei Zhang*, “UHR-DeepFMT: Ultra-high spatial resolution reconstruction of fluorescence molecular tomography based on 3D fusion dual-sampling deep neural network,” IEEE Transactions on Medical Imaging, 2021, 40(11): 3217–3228. (SCI, Q1, IF=10.6)

[50]  Peng Zhang, Chenbin Ma, Yangyang Sun, Guangda Fan, Fan Song, Youdan Feng, Guanglei Zhang*, “Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings,” Computers in Biology and Medicine, 2021, 139: 104880. (SCI, Q1, IF=6.1)

[51]  Xiangyu Zhao, Peng Zhang, Fan Song, Guangda Fan, Yangyang Sun, Yujia Wang, Zheyuan Tian, Luqi Zhang, Guanglei Zhang*, “D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution,” Computers in Biology and Medicine, 2021, 135: 104526. (SCI, Q1, IF=7.7)

[52]  Yang Gao, Fan Song, Peng Zhang, Jian Liu, Jingjing Cui, Yingying Ma, Guanglei Zhang*, and J. Luo, “Improving the subtype classification of non-small cell lung cancer by elastic deformation based machine learning,” Journal of Digital Imaging, 2021, 34: 605–617. (SCI, Q1, IF=4.4)

[53]  Lan Song, Tongtong Xing, Zhenchen Zhu, Wei Han, Guangda Fan, Ji Li, Huayang Du, Wei Song, Zhengyu Jin, Guanglei Zhang, “Hybrid clinical-radiomics model for precisely predicting the invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule,” Academic Radiology, 2021, 28(9): e267–e277. (SCI, Q1, IF=4.8)

[54]  Ruixiang Liu, Zhongyu Cai, Qingsong Zhang, Heng Yuan, Guanglei Zhang, De’an Yang, “Colorimetric two-dimensional photonic crystal biosensors for label-free detection of hydrogen peroxide,” Sensors and Actuators B: Chemical, 2021, 354:131236. (SCI, Q1, IF=8.4)

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[56]  Wenjuan Cai, Yundai Chen, Jun Guo, Baoshi Han, Yajun Shi, Lei Ji, Jinliang Wang, Guanglei Zhang*, Jianwen Luo, “Accurate detection of atrial fibrillation from 12-Lead ECG using deep neural network,” Computers in Biology and Medicine, 2020, 116: 103378. (SCI, Q1, IF=7.7)

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