2023
Z. Xu, D. Lu, J. Yan, J. Sun, J. Luo, D. Wei, and & others. 2023. “Category-Level Regularized Unlabeled-to-Labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data.” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 3-13.
C. Chen, A. Zhong, D. Wu, J. Luo, and Q. Li. 2023. “Contrastive Masked Image-Text Modeling for Medical Visual Representation Learning.” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 493-503.
S. Kim, K. Kim, J. Hu, C. Chen, Z. Lyu, R. Hui, and & others. 2023. “MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation.” arXiv preprint arXiv:2309.13539.
J. Charton, H. Ren, S. Kim, C. M. Gonzalez, J. Khambhati, J. Cheng, and & others. 2023. “Multi-task Learning for Hierarchically-Structured Images: Study on Echocardiogram View Classification.” in International Workshop on Advances in Simplifying Medical Ultrasound, Pp. 185-194.
K. Gong, K. Johnson, G. El Fakhri, Q. Li, and T. Pan. 2023. “PET image denoising based on denoising diffusion probabilistic model.” European Journal of Nuclear Medicine and Molecular Imaging, Pp. 1-11.
K. Kim, F. Macruz, D. Wu, C. Bridge, S. McKinney, A. A. Al Saud, and & others. 2023. “Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis.” Physics in Medicine & Biology, 68, 20, Pp. 205013.
C. Tang, Z. Liu, C. Ma, Z. Wu, Y. Li, W. Liu, and & others. 2023. “PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models.” arXiv preprint arXiv:2309.10238.
Z. Liu, A. Zhong, Y. Li, L. Yang, C. Ju, Z. Wu, and & others. 2023. “Tailoring Large Language Models to Radiology: A Preliminary Approach to LLM Adaptation for a Highly Specialized Domain.” in International Workshop on Machine Learning in Medical Imaging, Pp. 464-473.
L. Zhang, Z. Liu, L. Zhang, Z. Wu, X. Yu, J. Holmes, and & others. 2023. “Segment Anything Model (SAM) for Radiation Oncology.” arXiv preprint arXiv:2306.11730.
K. Zhang, J. Yu, Z. Yan, Y. Liu, E. Adhikarla, S. Fu, and & others. 2023. “BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks.” arXiv preprint arXiv:2305.17100.
T. Yusufaly, E. Roncali, L. Strigari, J. Brosch-Lenz, G. E. Fakhri, P. Heidari, and & others. 2023. “Multiscale dosimetric and radiobiological modeling for radiopharmaceutical therapy (RPT), Part 1: Clinical overview and motivating examples.” Journal of Nuclear Medicine, 64, supplement 1, Pp. P358-P358.
Y. Xie, M. Yuan, B. Dong, and Q. Li. 2023. “Diffusion Model for Generative Image Denoising.” arXiv preprint arXiv:2302.02398.
Y. Xie, M. Yuan, B. Dong, and Q. Li. 2023. “Unsupervised Image Denoising with Score Function.” arXiv preprint arXiv:2304.08384.
Z. Wu, L. Zhang, C. Cao, X. Yu, H. Dai, C. Ma, and & others. 2023. “Exploring the trade-offs: Unified large language models vs local fine-tuned models for highly-specific radiology nli task.” arXiv preprint arXiv:2304.09138.
L. Strigari, E. Roncali, T. Yusufaly, J. Brosch-Lenz, G. E. Fakhri, P. Heidari, and & others. 2023. “Multiscale dosimetric and radiobiological modeling for radiopharmaceutical therapy (RPT), Part 4: digital twins for optimized RPT and their ethical and regulatory dimensions.” Journal of Nuclear Medicine, 64, supplement 1, Pp. P451-P451.
B. Saboury, T. Bradshaw, R. Boellaard, I. Buvat, J. Dutta, M. Hatt, and & others. 2023. “Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.” Journal of Nuclear Medicine, 64, 2, Pp. 188-196.
H. R. Roth, N. Rieke, S. Albarqouni, and Q. Li. 2023. “Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models.” IEEE Transactions on Medical Imaging, 42, 7, Pp. 1914-1919.
J. Luo, K. Kim, T. Delie, S. Esfahani, P. Heidari, B. Saboury, and & others. 2023. “Multi-Timepoint SPECT Image Registration for PSMA-Targeted Radiopharmaceutical Therapy Dosimetry.” Journal of Nuclear Medicine, 64, supplement 1, Pp. P1275-P1275.
Z. Liu, T. Zhong, Y. Li, Y. Zhang, Y. Pan, Z. Zhao, and & others. 2023. “Evaluating large language models for radiology natural language processing.” arXiv preprint arXiv:2307.13693.
Z. Liu, A. Zhong, Y. Li, L. Yang, C. Ju, Z. Wu, and & others. 2023. “Radiology-GPT: A Large Language Model for Radiology.” arXiv preprint arXiv:2306.08666.
Z. Liu, X. Yu, L. Zhang, Z. Wu, C. Cao, H. Dai, and & others. 2023. “DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4.” arXiv preprint arXiv:2303.11032.
Z. Liu, P. Wang, Y. Li, J. Holmes, P. Shu, L. Zhang, and & others. 2023. “RadOnc-GPT: A Large Language Model for Radiation Oncology.” arXiv preprint arXiv:2309.10160.
Z. Liu, Y. Li, P. Shu, A. Zhong, L. Yang, C. Ju, and & others. 2023. “Radiology-Llama2: Best-in-Class Large Language Model for Radiology.” arXiv preprint arXiv:2309.06419.
C. Liu, Z. Liu, J. Holmes, L. Zhang, L. Zhang, Y. Ding, and & others. 2023. “Artificial General Intelligence for Radiation Oncology.” arXiv preprint arXiv:2309.02590.
X. Li, L. Zhang, Z. Wu, Z. Liu, L. Zhao, Y. Yuan, and & others. 2023. “Artificial General Intelligence for Medical Imaging.” arXiv preprint arXiv:2306.05480.
G. Y. Li, J. Chen, S.-I. Jang, K. Gong, and Q. Li. 2023. “SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images.” arXiv preprint arXiv:2302.03861.
R. Hu, Y. Chen, K. Kim, M. A. B. C. Rockenbach, Q. Li, and H. Liu. 2023. “DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction.” arXiv preprint arXiv:2303.04661.
J. Hu, K. Deng, J. Wu, and Q. Li. 2023. “A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity.” arXiv preprint arXiv:2303.05410.
J. Hu, K. Deng, N. Li, and Q. Li. 2023. “Decentralized Riemannian natural gradient methods with Kronecker-product approximations.” arXiv preprint arXiv:2303.09611.
H. Dai, C. Ma, Z. Liu, Y. Li, P. Shu, X. Wei, and & others. 2023. “Samaug: Point prompt augmentation for segment anything model.” arXiv preprint arXiv:2307.01187.
H. Dai, Z. Liu, W. Liao, X. Huang, Z. Wu, L. Zhao, and & others. 2023. “Chataug: Leveraging chatgpt for text data augmentation.” arXiv preprint arXiv:2302.13007.
H. Dai, Z. Liu, W. Liao, X. Huang, Y. Cao, Z. Wu, and & others. 2023. “AugGPT: Leveraging ChatGPT for Text Data Augmentation.” arXiv preprint arXiv:2302.13007.
H. Dai, Y. Li, Z. Liu, L. Zhao, Z. Wu, S. Song, and & others. 2023. “AD-AutoGPT: An Autonomous GPT for Alzheimer’s Disease Infodemiology.” arXiv preprint arXiv:2306.10095.
H. Dai, M. Hu, Q. Li, L. Zhang, L. Zhao, D. Zhu, and & others. 2023. “Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer’s Disease Progression via Counterfactual Inference.” arXiv preprint arXiv:2307.01389.
C. Chen, J. Miao, D. Wu, Z. Yan, S. Kim, J. Hu, and & others. 2023. “MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation.” arXiv preprint arXiv:2309.08842.
H. Cai, X. Huang, Z. Liu, W. Liao, H. Dai, Z. Wu, and & others. 2023. “Exploring Multimodal Approaches for Alzheimer’s Disease Detection Using Patient Speech Transcript and Audio Data.” arXiv preprint arXiv:2307.02514.
H. Cai, X. Huang, Z. Liu, W. Liao, H. Dai, Z. Wu, and & others. 2023. “Multimodal Approaches for Alzheimer’s Detection Using Patients’ Speech and Transcript.” in International Conference on Brain Informatics, Pp. 395-406.
2022
A. Zhong, H. He, Z. Ren, N. Li, and Q. Li. 2022. “FedDAR: Federated Domain-Aware Representation Learning.” arXiv preprint arXiv:2209.04007.
M. Zhang, B. Dong, and Q. Li. 2022. “MS-GWNN: multi-scale graph wavelet neural network for breast cancer diagnosis.” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Pp. 1-5.
M. Zabihi, D. B. Rubin, S. E. Ack, E. J. Gilmore, V. M. Junior, S. F. Zafar, and & others. 2022. “Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury.” bioRxiv, Pp. 2022.09. 30.510334.
P. You, X. Li, F. Zhang, and Q. Li. 2022. “Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution.” BME Frontiers, 2022, Pp. 9814824.
P. Xu, K. Kim, H. Liu, and Q. Li. 2022. “Attention-Fused CNN Model Compression with Knowledge Distillation for Brain Tumor Segmentation.” in Annual Conference on Medical Image Understanding and Analysis, Cham, Pp. 328-338.
Y. Xie, D. Wu, B. Dong, and Q. Li. 2022. “Trained model in supervised deep learning is a conditional risk minimizer.” arXiv preprint arXiv:2202.03674.
Y. Xie, and Q. Li. 2022. “A review of deep learning methods for compressed sensing image reconstruction and its medical applications.” Electronics, 11, 4, Pp. 586.
Y. Xie, and Q. Li. 2022. “Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction.” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 655-664.
J. Se-In, P. Tinsu, L. Gary Ye, C. Junyu, L. Quanzheng, and G. Kuang. 2022. “PET image denoising based on transformer: evaluations on datasets of multiple tracers.” Journal of Nuclear Medicine, 63, supplement 2, Pp. 2257.
B. Saboury, T. Bradshaw, R. Boellaard, I. Buvat, J. Dutta, M. Hatt, and & others. 2022. “Artificial Intelligence Ecosystem in Nuclear Medicine: Opportunities, Challenges, and Responsibilities.” Journal of Nuclear Medicine, 63, supplement 2, Pp. 2733-2733.
M. A. B. C. Rockenbach, D. Buric, J. Carness, C. Burke, J. Wollborn, S. Sami, and & others. 2022. “169: Comparing ai and clinicians in oxygenation device management for suspected COVID-19 ICU patients.” Critical Care Medicine, 50, 1, Pp. 68.
S. Rezayi, H. Dai, Z. Liu, Z. Wu, A. Hebbar, A. H. Burns, and & others. 2022. “Clinicalradiobert: Knowledge-infused few shot learning for clinical notes named entity recognition.” in International Workshop on Machine Learning in Medical Imaging, Pp. 269-278.
A. Rahmim, T. J. Bradshaw, I. Buvat, J. Dutta, A. K. Jha, P. E. Kinahan, and & others. 2022. “Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine–The Bethesda Report (AI Summit 2022).” arXiv preprint arXiv:2211.03783.
H. Liang, Y. Guo, X. Chen, K.-L. Ang, Y. He, N. Jiang, and & others. 2022. “Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.” European Radiology, 32, 4, Pp. 2235-2245, 2022/04/01.
Y. Li, J. Cui, J. Chen, G. Zeng, S. Wollenweber, F. Jansen, and & others. 2022. “A Noise-Level-Aware Framework for PET Image Denoising.” in International Workshop on Machine Learning for Medical Image Reconstruction, Pp. 75-83.
Y. Li, J. Chen, S.-i. Jang, K. Gong, and Q. Li. 2022. “Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation.” arXiv preprint arXiv:2212.10724.
D. Li, H. Ren, D. J. Varelmann, P. Sarin, P. Xu, D. Wu, and & others. 2022. “Risk assessment for acute kidney injury and death among new COVID-19 positive adult patients without chronic kidney disease: retrospective cohort study among three US hospitals.” BMJ open, 12, 2, Pp. e053635.
Y.-G. Kim, K. Kim, D. Wu, H. Ren, W. Y. Tak, S. Y. Park, and & others. 2022. “Deep learning-based four-region lung segmentation in chest radiography for COVID-19 diagnosis.” Diagnostics, 12, 1, Pp. 101.
S.-I. Jang, T. Pan, Y. Li, P. Heidari, J. Chen, Q. Li, and & others. 2022. “Spach Transformer: Spatial and channel-wise transformer based on local and global self-attentions for PET image denoising.” arXiv preprint arXiv:2209.03300.
Y. Han, D. Wu, K. Kim, and Q. Li. 2022. “End-to-end deep learning for interior tomography with low-dose x-ray CT.” Physics in Medicine & Biology, 67, 11, Pp. 115001.
K. Gong, P. Han, G. El Fakhri, C. Ma, and Q. Li. 2022. “Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.” NMR in Biomedicine, 35, 4, Pp. e4224.
J. Cui, Y. Xie, A. A. Joshi, K. Gong, K. Kim, Y.-D. Son, and & others. 2022. “PET Denoising and Uncertainty Estimation Based on NVAE Model Using Quantile Regression Loss.” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 173-183.
J. Cui, Y. Xie, K. Gong, K. Kim, Y. D. Son, J. H. Kim, and & others. 2022. “2.5D Nouveau VAE model for 11C-DASB PET image denoising and uncertainty estimation.” Journal of Nuclear Medicine, 63, supplement 2, Pp. 3223-3223.
J. Cui, K. Gong, P. Han, H. Liu, and Q. Li. 2022. “Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.” Medical Physics, 49, 4, Pp. 2373-2385.
J. Cui, K. Gong, N. Guo, K. Kim, H. Liu, and Q. Li. 2022. “Unsupervised pet logan parametric image estimation using conditional deep image prior.” Medical Image Analysis, 80, Pp. 102519.
J. Charton, H. Ren, J. Khambhati, J. DeFrancesco, J. Cheng, A. A. Waheed, and & others. 2022. “View Classification of Color Doppler Echocardiography via Automatic Alignment Between Doppler and B-Mode Imaging.” in Simplifying Medical Ultrasound: Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Proceedings, Cham, Pp. 64-71.
H. Cai, W. Liao, Z. Liu, X. Huang, Y. Zhang, S. Ding, and & others. 2022. “Coarse-to-fine knowledge graph domain adaptation based on distantly-supervised iterative training.” arXiv preprint arXiv:2211.02849.
C. Burke, J. Wollborn, S. Sami, D. Buric, J. Carness, M. A. B. C. Rockenbach, and & others. 2022. “1127: Evaluating agreement across clinicians in predicting oxygen requirements for critically ill patients.” Critical Care Medicine, 50, 1, Pp. 562.
T. J. Bradshaw, R. Boellaard, J. Dutta, A. K. Jha, P. Jacobs, Q. Li, and & others. 2022. “Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development.” Journal of Nuclear Medicine, 63, 4, Pp. 500-510.
T. Bradshaw, R. Boellaard, J. Dutta, A. Jha, P. Jacobs, Q. Li, and & others. 2022. “Pitfalls in the development of artificial intelligence algorithms in nuclear medicine and how to avoid them.” Journal of Nuclear Medicine, 63, supplement 2, Pp. 2724-2724.
J. Ahn, K. Kim, J. Koh, and Q. Li. 2022. “Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning .” arXiv preprint arXiv:2202.00195.
2021
A. Zhong, X. Li, D. Wu, H. Ren, K. Kim, Y. Kim, and & others. 2021. “Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.” Medical Image Analysis, 70, Pp. 101993.
P. You, X. Li, Z. Wang, H. Wang, B. Dong, and Q. Li. 2021. “Characterization of brain iron deposition pattern and its association with genetic risk factor in alzheimer’s disease using susceptibility-weighted imaging.” Frontiers in Human Neuroscience, 15, Pp. 654381.
W. Xue, J. Li, Z. Hu, E. Kerfoot, J. Clough, I. Oksuz, and & others. 2021. “Left ventricle quantification challenge: A comprehensive comparison and evaluation of segmentation and regression for mid-ventricular short-axis cardiac MR data.” IEEE Journal of Biomedical and Health Informatics, 25, 9, Pp. 3541-3553.
P. Xu, K. Kim, J. Koh, D. Wu, Y. R. Lee, S. Y. Park, and & others. 2021. “Efficient knowledge distillation for liver CT segmentation using growing assistant network.” Physics in Medicine & Biology, 66, 23, Pp. 235005.
M. Xu, D. L. Sanz, P. Garces, F. Maestu, Q. Li, and D. Pantazis. 2021. “A graph Gaussian embedding method for predicting Alzheimer’s disease progression with MEG brain networks.” IEEE Transactions on Biomedical Engineering, 68, 5, Pp. 1579-1588.
N. Xie, K. Gong, N. Guo, Z. Qin, Z. Wu, H. Liu, and & others. 2021. “Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network.” Neuroimage, 240, Pp. 118380.
D. Wu, Y. Xie, and Q. Li. 2021. “Uncertainty Prediction for Deep Learning-based Image Denoising in Low-dose CT Imaging.” in 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Pp. 1-2.
D. Wu, K. Kim, and Q. Li. 2021. “Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning.” Medical Physics, 48, 12, Pp. 7657-7672.
K. Gong, D. Wu, C. D. Arru, F. Homayounieh, N. Neumark, J. Guan, and & others. 2021. “A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.” European journal of radiology, 139, Pp. 109583.
K. Gong, K. Kim, J. Cui, D. Wu, and Q. Li. 2021. “The evolution of image reconstruction in PET: From filtered back-projection to artificial intelligence.” PET clinics, 16, 4, Pp. 533-542.
K. Gong, P. K. Han, K. A. Johnson, G. El Fakhri, C. Ma, and Q. Li. 2021. “Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.” European journal of nuclear medicine and molecular imaging, 48, Pp. 1351-1361.
K. Gong, C. Catana, J. Qi, and Q. Li. 2021. “Direct reconstruction of linear parametric images from dynamic PET using nonlocal deep image prior.” IEEE Transactions on Medical Imaging, 41, 3, Pp. 680-689.
M. Flores, I. Dayan, H. Roth, A. Zhong, A. Harouni, A. Gentili, and & others. 2021. “Federated Learning used for predicting outcomes in SARS-COV-2 patients.” Research Square.
W. Fang, D. Wu, K. Kim, M. K. Kalra, R. Singh, L. Li, and & others. 2021. “Iterative material decomposition for spectral CT using self-supervised Noise2Noise prior.” Physics in Medicine & Biology, 66, 15, Pp. 155013.
S. Ebrahimian, F. Homayounieh, M. A. Rockenbach, P. Putha, T. Raj, I. Dayan, and & others. 2021. “Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.” Scientific Reports, 11, 1, Pp. 858.
I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, and & others. 2021. “Federated learning for predicting clinical outcomes in patients with COVID-19.” Nature medicine, 27, 10, Pp. 1735-1743.
J. Cui, Y. Xie, K. Gong, K. Kim, J. Yang, P. Larson, and & others. 2021. “Pet denoising and uncertainty estimation based on NVAE model.” in 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Pp. 1-3.
J. Cui, K. Gong, N. Guo, C. Wu, K. Kim, H. Liu, and & others. 2021. “Populational and individual information based PET image denoising using conditional unsupervised learning.” Physics in Medicine & Biology, 66, 15, Pp. 155001.
J. Cui, K. Gong, N. Guo, S. Wollenweber, F. Jansen, H. Liu, and & others. 2021. “SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising.” in 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Pp. 1-3.
V. Buch, A. Zhong, X. Li, M. A. B. C. Rockenbach, D. Wu, H. Ren, and & others. 2021. “Development and validation of a deep learning model for prediction of severe outcomes in suspected COVID-19 Infection.” arXiv preprint arXiv:2103.11269.
2020
F. Yu, H. Dong, M. Zhang, J. Zhao, Dong. B., Q. Li, and L. Zhang. 2020. “AF-SEG: An Annotation-Free Approach for Image Segmentation by Self-Supervision and Generative Adversarial Network.” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Pp. 1503-1507.
H. Dong, F. Yu, Jiang. H., H. Zhang, Dong. B., Q. Li, and Zhang. L. 2020. “Annotation-Free Gliomas Segmentation Based on a Few Labeled General Brain Tumor Images.” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) , Pp. 354-358.
M. Zhang, J. Zhao, X. Li, L. Zhang, and Q. Li. 2020. “Ascnet: Adaptive-scale convolutional neural networks for multi-scale feature learning.” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Pp. 144.
M. Zhang, X. Li, M. Xu, and Q. Li. 2020. “Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease.” IEEE Journal of Biomedical and Health Informatics, 24, Pp. 3095.
S. Wang, L. Liu, J. Liu, L. Miao, Q. Zhuang, N. Guo, and G. Ren. 2020. “Characteristics of prescriptions and costs for acute upper respiratory tract infections in Chinese outpatient pediatric patients: a nationwide cross-sectional study.” BMC Complementary Medicine and Therapies, 20, 1, Pp. 1-10.
N. Xie, K. Gong, N. Guo, Z. Qin, J. Cui, Z. Wu, H. Liu, and Q. Li. 2020. “Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network.” In Miccai2020 International Conference on Medical Image Computing and Computer-Assisted Intervention.
D. Wu, K. Kim, and Q. Li. 2020. “Digital Breast Tomosynthesis Reconstruction with Deep Neural Network for Improved Contrast and In-Depth Resolution.” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Pp. 656-659.
Q. Dong, N. Qiang, J. Ly, X. Li, T. Liu, and Q. Li. 2020. “Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE).” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Proceedings, Part VII, 23, Pp. 498.
W. Qiu, Y. Huang, and Q. Li. 2020. “IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks.” 2020 IEEE International Conference on Big Data , Pp. 4715-4723.
W. Qiu, J. Guo, X. Li, M. Xu, M. Zhang, N. Guo, and Q. Li. 2020. “Multi-label detection and classification of red blood cells in microscopic images.” in 2020 IEEE International Conference on Big Data (Big Data), Pp. 4257.
Q. Dong, N. Qiang, J. Lv, X. Li, L. Dong, T. Liu, and Q Li, Z. 2020. “A Novel fMRI Representation Learning Framework with GAN.” in Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings 11, Pp. 21.
D. Wu, K. Gong, C.D. Arru, F Homayounieh, B. Bizzo, V. Buch, and & others. 2020. “Severity and consolidation quantification of COVID-19 from CT images using deep learning based on hybrid weak labels.” IEEE Journal of Biomedical and Health Informatics, 24, 12, Pp. 3529-3538.
S. Jeong, X. Li, J. Yang, Q. Li, and V. Tarokh. 2020. “Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study.” IEEE Access, 8, Pp. 36728.
Q. Dong, Qiang. N., J. Ly, X. Li, T. Liu, and Q. Li. 2020. “Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification.” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Proceedings, Part VII, 23, Pp. 508.
2019
Z. Guo, N. Guo, K. Gong, and Q. Li. 2019. “Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net.” Medical Imaging, Pp. 1095009.
D. Wu, K. Kim, and Q. Li. 2019. “Computationally efficient deep neural network for computed tomography image reconstruction.” Medical physics.
J. Cui, K. Gong, N. Guo, K. Kim, H. Liu, and Q. Li. 2019. “CT-guided PET parametric image reconstruction using deep neural network without prior training data.” Medical Imaging, Pp. 109480Z.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li. 2019. “Deep Learning-Based Image Segmentation on Multimodal Medical Imaging.” IEEE Transactions on Radiation and Plasma Medical Sciences, 3, Pp. 162-169.
K. Gong, C. Catana, J. Qi, and Q. Li. 2019. “Direct Patlak Reconstruction for Low-Dose Dynamic PET Using Unsupervised Deep Learning.” Nuclear Medicine, 60, Pp. 575-575.
K. Gong, C. Catana, J. Qi, and Q. Li. 2019. “Direct patlak reconstruction from dynamic PET using unsupervised deep learning.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110720R.
R. Ju, C. Hu, P. Zhou, and Q. Li. 2019. “Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning.” IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),, 16, Pp. 244-257.
K. Gong, D. Wu, K. Kim, J. Yang, G. El Fakhri, Y. Seo, and et al. 2019. “EMnet: an unrolled deep neural network for PET image reconstruction.” Medical Imaging, Pp. 1094853.
N. Guo, C. Wu, Z. Guo, and Q. Li. 2019. “Intratumoral heterogeneity predicts recurrence after radiofrequency ablation therapy using early post-treatment 18F-FDG PET in lung cancer.” Journal of Nuclear Medicine, 60, Pp. 1588-1588.
D. Wu, K. Kim, M. K. Kalra, B. De Man, and Q. Li. 2019. “Learned primal-dual reconstruction for dual energy computed tomography with reduced dose.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 1107206.
K. Gong, K. Kim, D. Wu, M. K. Kalra, and Q Li, Z. 2019. “Low-dose dual energy CT image reconstruction using non-local deep image prior.” In IEEE Nuclear Science Symposium and Medical Imaging Conference , Pp. 1-2.
K. Gong, D. Wu, K. Kim, J. Yang, T. Sun, G. El Fakhri, and et al. 2019. “MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110720O.
K. Kim, Y. D. Son, J.-H. Kim, and Q. Li. 2019. “Parametric image estimation using Residual simplified reference tissue model.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 1107237.
T.-A. Song, F. Yang, S. R. Chowdhury, K. Kim, K. A. Johnson, and G. El Fakhri. 2019. “PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior.” IEEE Transactions on Computational Imaging.
J. Cui, K. Gong, N. Guo, C. Wu, K. Kim, and H. Liu. 2019. “Population and individual information guided PET image denoising using deep neural network.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110721E.
T.-A. Song, S. R. Chowdhury, G. El Fakhri, Q. Li, and J. Dutta. 2019. “Super-resolution PET imaging using a generative adversarial network.” Journal of Nuclear Medicine, 60, Pp. 576-576.
K. Kim, D. Kim, J. Yang, G. El Fakhri, Y. Seo, J. A. Fessler, and et al. 2019. “Time of flight PET reconstruction using nonuniform update for regional recovery uniformity.” Medical physics, 46, Pp. 649-664.
D. Wu, K. Kim, G. El Fakhri, and Q. Li. 2019. “Computational-efficient cascaded neural network for CT image reconstruction.” Physics of Medical Imaging, Pp. 109485Z.
X. Li, J. H. Thrall, S. R. Digumarthy, M. K. Kalra, P. V. Pandharipande, B. Zhang, C. Nitiwarangkul, R. Singh, R. D. Khera. Q. Li. 2019. “Deep learning-enabled system for rapid pneumothorax screening on chest CT.” European Journal of Radiology, 120, Pp. 108692.
Y. Zhao, X. Li, H. Huang, W. Zhang, S. Zhao, M. Makkie, M. Zhang, Q. Li. T. Liu. 2019. “4D Modeling of fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN).” IEEE transactions on cognitive and developmental systems, 12, Pp. 451.
X. Li, N. Guo. Q. Li. 2019. “Functional neuroimaging in the new era of big data.” Genomics, Proteomics & Bioinformatics, 17, Pp. 393.
2018
J. H. Thrall, X. Li, Q. Li, C. Cruz, S. Do, and K. Dreyer. 2018. “Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success.” Journal of the American College of Radiology, 15, Pp. 504-508.
F. Yang, R. Tabassum, J. Sanchez, A. Becker, G. El Fakhri, Q. Li, and et al. 2018. “Association between partial volume corrected longitudinal tau measures and cognitive decline,” Journal of Nuclear Medicine, 59, Pp. 411-411.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.” Physics in Medicine & Biology, 63, Pp. 125011.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Attenuation Correction of PET/MR Using Deep Neural Network Based on Dixon and ZTE MR Images.” Journal of Nuclear Medicine, 59, Pp. 650-650.
L. Zhang, H. Wang, Q. Li, M.-H. Zhao, and Q.-M. Zhan. 2018. “Big data and medical research in China.” bmj, 360, Pp. j5910.
X. WANG, X. Zhen, Q. Li, D. Shen, and H. Huang. 2018. “Cognitive assessment prediction in Alzheimer’s disease by multi-layer multi-target regression.” Neuroinformatics, 16, Pp. 285-294.
D. Wu, K. Kim, and Q. Li. 2018. “Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging.” arXiv preprint arXiv:1810.03999.
D. Pantazis, M. Fang, S. Qin, Y. Mohsenzadeh, Q. Li, and R. M. Cichy. 2018. “Decoding the orientation of contrast edges from MEG evoked and induced responses.” NeuroImage, 180, Pp. 267-279.
D. Pantazis, M. Fang, S. Qin, Y. Mohsenzadeh, Q. Li, and R. M. Cichy. 2018. “Decoding the orientation of contrast edges from MEG evoked and induced responses.” NeuroImage, 180, Pp. 267-279.
D. Wu, K. Kim, B. Dong, G. El Fakhri, and Q. Li. 2018. “End-to-End Lung Nodule Detection in Computed Tomography.” International Workshop on Machine Learning in Medical Imaging, Pp. 37-45.
P. Bandi, O. Geessink, Q. Manson, M. Van Dijk, M. Balkenhol, and M. Herms. 2018. “From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge.” IEEE transactions on medical imaging, 38, Pp. 550-560.
K. Gong, J. Guan, K. Kim, X. Zhang, J. Yang, Y. Seo, and et al. 2018. “Iterative PET image reconstruction using convolutional neural network representation.” IEEE Transactions on Medical Imaging, 38, Pp. 675-685.
F. Yang, R. Tabassum, A. Becker, J. S. Sanchez, G. El Fakhri, Q. Li, and et al. 2018. “JOINT DEBLURRING OF LONGITUDINAL DIFFERENTIAL PET IMAGES OF TAU.” Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 14, Pp. P167.
K. Gong, K. Kim, J. Cui, N. Guo, C. Catana, J. Qi, and et al. 2018. “Learning personalized representation for inverse problems in medical imaging using deep neural network.” arXiv preprint arXiv:1807.01759.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Learning personalized representation for inverse problems in medical imaging using deep neural network.” Physics in Medicine & Biology, 63, Pp. 125011.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li. 2018. “Medical image segmentation based on multi-modal convolutional neural network: study on image fusion schemes.” In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Pp. 903-907.
Y. Zhao, X. Li, W. Zhang, S. Zhao, M. Makkie, M. Zhang, and et al. 2018. “Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN).” In International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 181-189.
X. Li, Q. Chen, X. WANG, N. Guo, N. Wu, and Q. Li. 2018. “Network Modeling and Pathway Inference from Incomplete Data (” PathInf”).” arXiv preprint arXiv:1810.00839.
J. Sepulcre, M. J. Grothe, F. O. d. Uquillas, L. Ortiz-Terán, I. Diez, and H.-S. Yang. 2018. “Neurogenetic contributions to amyloid beta and tau spreading in the human cortex.” Nature medicine, 24, Pp. 1910.
K. Kim, J. Dutta, A. Groll, G. El Fakhri, L.-J. Meng, and Q. Li. 2018. “A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET.” Physics in Medicine & Biology, 63, Pp. 165011.
K. Kim, D. Wu, K. Gong, J. Dutta, J. H. Kim, Y. D. Son, and et al. 2018. “Penalized PET reconstruction using deep learning prior and local linear fitting,” IEEE Transactions on Medical Imaging, 37, Pp. 1478-1487.
K. Gong, C. Catana, J. Qi, and Q. Li. 2018. “PET Image Reconstruction Using Deep Image Prior.” IEEE Transactions on Medical Imaging.
M. Zhang, X. Li, M. Xu, and Q. Li. 2018. “RBC semantic segmentation for sickle cell disease based on deformable U-Net.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 695-702.
Y. Chang, G. C. Sharp, Q. Li, H. A. Shih, G. El Fakhri, J. B Ra, and et al. 2018. “Subject-specific brain tumor growth modelling via an efficient Bayesian inference framework.” Medical Imaging 2018: Image Processing, Pp. 105742I.
2017
B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, JAWM. van der Laak, CAMELYON16 the Consortium, M. Hermsen, QF. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, MC. van Dijk, P. Bult, F. Beca, AH. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, and Q. Li. 2017. “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.” JAMA, 318, 22, Pp. 2199.