Publications

2025

Bradshaw, T. J., Tie, X., Warner, J., Hu, J., Li, Q., & Li, X. (2025). Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians. Journal of Nuclear Medicine, 66(2), 173–182.

Charton, J., Zhang, T., Li, N., & Li, Q. (2025). Enhancing gaze estimation accuracy in wearable eye-tracking devices using neural networks. Neural Computing and Applications, 37(21), 16703–16714. https://doi.org/10.1007/s00521-025-11334-y

Chen, Z., Yoon, S., Strotzer, Q., Khalid, R. N., Tivnan, M., Li, Q., Gupta, R., & Wu, D. (2025). Portable head CT motion artifact correction via diffusion-based generative model. Computerized Medical Imaging and Graphics, 119, 102478.

Dai, H., Li, Y., Liu, Z., Zhao, L., Wu, Z., Song, S., Ye, S., Zhu, D., Li, X., Li, S., Yao, X., Shi, L., Peng, T.-Q., Li, Q., Chen, Z., Zhang, D., Liu, T., & Mai, G. (2025). Ad-autogpt: An autonomous gpt for alzheimer’s disease infodemiology. PLOS Global Public Health, 5(5), e0004383.

Dai, H., Liu, Z., Liao, W., Huang, X., Cao, Y., Wu, Z., Zhao, L., Xu, S., Zeng, F., Liu, W., Liu, N., Li, S., Zhu, D., Cai, H., Sun, L., Li, Q., Shen, D., Liu, T., & Li, X. (2025). Auggpt: Leveraging chatgpt for text data augmentation. IEEE Transactions on Big Data. https://ieeexplore.ieee.org/abstract/document/10858342/

Dong, B., Zhang, L., Yuan, J., Chen, Y., Li, Q., & Shen, L. (2025). Large language models: Game-changers in the healthcare industry. Science Bulletin, 70(3), 283–286.

Hu, J., Deng, K.-K., & Li, Q.-Z. (2025). Decentralized Riemannian Natural Gradient Methods with Kronecker Product Approximations. Journal of the Operations Research Society of China. https://doi.org/10.1007/s40305-025-00583-2

Kim, S., Jin, P., Chen, C., Kim, K., Lyu, Z., Ren, H., Kim, S., Liu, Z., Zhong, A., Liu, T., Li, X., & Li, Q. (2025). MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography. IEEE Journal of Biomedical and Health Informatics. https://ieeexplore.ieee.org/abstract/document/10878483/

Kim, S., Jin, P., Song, S., Chen, C., Li, Y., Ren, H., Li, X., Liu, T., & Li, Q. (2025). Echofm: Foundation model for generalizable echocardiogram analysis. IEEE Transactions on Medical Imaging. https://ieeexplore.ieee.org/abstract/document/11040094/

Lei, B., Yang, F., Song, T., Zhou, Z., Balaji, V., Liu, Z., Li, Q., King, M., & Dutta, J. (2025). Dosiomics-Based Prognostic Modeling for Lu-177-PSMA Therapy for Prostate Cancer. Society of Nuclear Medicine. https://jnm.snmjournals.org/content/66/supplement_1/252083.abstract

Li, X., Chen, W., Ren, H., Oh, Y., Cao, Y., Sharaf, E., Luo, J., Zhou, H.-Y., Sun, L., Liu, T., Shen, L., Li, Q., & Yuan, Y. (2025). Bridging Medical Imaging and Reports: Learning Radiologist’s Nuances via Fine-Grained Multi-Modal Alignment. https://www.researchsquare.com/article/rs-6002276/latest

Liu, Z., Li, Y., Shu, P., Zhong, A., Jiang, H., Pan, Y., Yang, L., Ju, C., Wu, Z., Ma, C., Chen, C., Kim, S., Dai, H., Zhao, L., Sun, L., Zhu, D., Liu, J., Liu, W., Shen, D., Li, Q., Liu, T.,  Li, X. (2025). Radiology-GPT: A large language model for radiology. Meta-Radiology, 100153.

Meng, R., Clement, C., Seifert, R., Ferdinandus, J., Li, X., Li, Q., Song, S., Oh, Y., Tian, Y., & Rominger, A. (2025). Radiomic Feature Similarity Predicts Treatment Response: Assessing Inter-Tumor Heterogeneity. Society of Nuclear Medicine.https://jnm.snmjournals.org/content/66/supplement_1/251883.abstract

Meng, R., Song, S., Li, X., Li, Q., Shen, D., & Guo, N. (2025). Graph-Based Radiomic Features Enhance cancer diagnosis and treatment evaluation in PET Imaging. Society of Nuclear Medicine. https://jnm.snmjournals.org/content/66/supplement_1/251881.abstract

Oh, Y., Jin, P., Park, S., Kim, S., Yoon, S., Kim, K., Kim, J. S., Li, X., & Li, Q. (2025). Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective (No. arXiv:2502.00619). arXiv. https://doi.org/10.48550/arXiv.2502.00619

Oh, Y., Seifert, R., Cao, Y., Clement, C., Ferdinandus, J., Lapa, C., Liebich, A., Amon, M., Enke, J., Song, S., Meng, R., Zeng, F., Guo, N., Li, X., Heidari, P., Rominger, A., Shi, K., & Li, Q. (2025). Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging (No. arXiv:2503.02824). arXiv. https://doi.org/10.48550/arXiv.2503.02824

Shi, Y., Li, Q., Sun, J., Li, X., & Liu, N. (2025). Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data (No. arXiv:2502.14044). arXiv. https://doi.org/10.48550/arXiv.2502.14044

Shi, Y., Yang, T., Chen, C., Li, Q., Liu, T., Li, X., & Liu, N. (2025). SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering? (No. arXiv:2502.13233). arXiv. https://doi.org/10.48550/arXiv.2502.13233

Sobieski, B., Tivnan, M., Wang, Y., Yoon, S., Jin, P., Wu, D., Li, Q., & Biecek, P. (2025). System-Embedded Diffusion Bridge Models (No. arXiv:2506.23726). arXiv. https://doi.org/10.48550/arXiv.2506.23726

Song, S., Yoon, S., Jin, P., Kim, S., Tivnan, M., Oh, Y., Meng, R., Chen, L., Lyu, Z., Wu, D., Guo, N., Li, X., & Li, Q. (2025). OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging (No. arXiv:2505.04899). arXiv. https://doi.org/10.48550/arXiv.2505.04899

Tian, Y., Jin, P., Yuan, M., Li, N., Zeng, B., & Li, Q. (2025). RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models (No. arXiv:2507.12201). arXiv. https://doi.org/10.48550/arXiv.2507.12201

Tian, Y., Song, S., Seifert, R., Clement, C., Li, Q., Rominger, A., Shi, K., Zeng, B., & Guo, N. (2025). Parameter-Refined PBPK Modeling for Enhanced Dosimetry in 177Lu-PSMA Radiopharmaceutical Therapy Using Estimated and Personalized Parameters. Society of Nuclear Medicine. https://jnm.snmjournals.org/content/66/supplement_1/251649.abstract

Tivnan, M., Kikkert, I. D., Wu, D., Yang, K., Wolterink, J. M., Li, Q., & Gupta, R. (2025). Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model. Scientific Reports, 15(1), 26002.

Tivnan, M., & Li, Q. (2025). Generative super-resolution PET imaging with Fourier diffusion models. Medical Imaging 2025: Physics of Medical Imaging, 13405, 196–201. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13405/134050W/Generative-super-resolution-PET-imaging-with-Fourier-diffusion-models/10.1117/12.3048849.short

Tivnan, M., Wu, D., & Li, Q. (2025). Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data (No. arXiv:2502.15064). arXiv. https://doi.org/10.48550/arXiv.2502.15064

Wang, C., Jiang, Y., Peng, Z., Li, C., Bang, C., Zhao, L., Lv, J., Sepulcre, J., Yang, C., He, L., Liu, T., Barron, D., Li, Q., Hirschtick, R., Kim, B.-H., Li, X., & Yuan, Y. (2025). Towards a general-purpose foundation model for fMRI analysis(No. arXiv:2506.11167). arXiv. https://doi.org/10.48550/arXiv.2506.11167

Wang, P., Liu, Z., Li, Y., Holmes, J., Shu, P., Zhang, L., Li, X., Li, Q., Laughlin, B. S., Toesca, D. S., Vargas, C. E., Vora, S. A., Patel, S. H., Sio, T. T., Liu, T., & Liu, W. (2025). Fine‐tuning open‐source large language models to improve their performance on radiation oncology tasks: A feasibility study to investigate their potential clinical applications in radiation oncology. Medical Physics, 52(7), e17985. https://doi.org/10.1002/mp.17985

Wang, Y., Jin, P., Yoon, S., Tivnan, M., Li, Q., Zhang, L., Chen, Z., & Wu, D. (2025). Projection Embedded Schrödinger Bridge for CT Sparse View Reconstruction. Proceedings of SPIE–the International Society for Optical Engineering, 13405, 1340520. https://pmc.ncbi.nlm.nih.gov/articles/PMC12082703/

Wang, Y., Jin, P., Yoon, S., Tivnan, M., Zhang, S., Zhang, L., Li, Q., Chen, Z., & Wu, D. (2025). Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data (No. arXiv:2510.22605). arXiv. https://doi.org/10.48550/arXiv.2510.22605

Wang, Y., Yoon, S., Jin, P., Tivnan, M., Song, S., Chen, Z., Hu, R., Zhang, L., Li, Q., & Chen, Z. (2025). Implicit Image-to-Image Schrödinger Bridge for image restoration. Pattern Recognition, 165, 111627.

Wu, D., Kim, K., & Li, Q. (2025, April 29). Systems and methods for artifact reduction in tomosynthesis with deep learning image processing. Google Patents. https://patents.google.com/patent/US12285284B2/en

Wu, D., Song, S., Wang, Y., Torolski, K. J., Ren, H., Rockenbach, M. A., Srinivasan, K., Rich, K. A., Kaushik, S., Cozzini, C., Wiesinger, F., Li, Q., & Hong, T. (2025). CT synthesis from MRI using 3D swin UNETR and distillation for upper abdominal radiotherapy treatment planning. Medical Imaging 2025: Physics of Medical Imaging, 13405, 563–567. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13405/134052Q/CT-synthesis-from-MRI-using-3D-swin-UNETR-and-distillation/10.1117/12.3048353.short

Wu, Z., Zhang, L., Cao, C., Yu, X., Liu, Z., Zhao, L., Li, Y., Dai, H., Ma, C., Li, G., Liu, W., Li, Q., Shen, D., Li, X., Zhu, D., & Liu, T. (2025). Exploring the trade-offs: Unified large language models vs local fine-tuned models for highly-specific radiology nli task. IEEE Transactions on Big Data. https://ieeexplore.ieee.org/abstract/document/10887002/

Xia, M., Bayerlein, R., Chemli, Y., Liu, X., Ouyang, J., Lin, M., El Fakhri, G., Badawi, R. D., Li, Q., & Liu, C. (2025). On Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report). Journal of Nuclear Medicine. https://jnm.snmjournals.org/content/early/2025/11/06/jnumed.125.270653.abstract

Xia, M., Bayerlein, R., Chemli, Y., Liu, X., Ouyang, J., Lin, M., Fakhri, G. E., Badawi, R. D., Li, Q., & Liu, C. (2025). On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report) (No. arXiv:2506.13995). arXiv. https://doi.org/10.48550/arXiv.2506.13995

Xia, M., Ko, K.-Y., Wang, D.-S., Chen, M.-K., Liu, Q., Xie, H., Guo, L., Ji, W., Ouyang, J., Bayerlein, R., Spencer, B. A., Li, Q., Badawi, R. D., Fakhri, G. E., & Liu, C. (2025). Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging (No. arXiv:2503.13257). arXiv. https://doi.org/10.48550/arXiv.2503.13257

Xia, M., Xie, H., Liu, Q., Zhou, B., Wang, H., Li, B., Rominger, A., Li, Q., Badawi, R. D., & Shi, K. (2025). LeqMod: Adaptable lesion-quantification-consistent modulation for deep learning low-count PET image denoising. IEEE Transactions on Medical Imaging. https://ieeexplore.ieee.org/abstract/document/11194243/

Xu, Z., Chen, C., Lu, D., Sun, J., Wei, D., Zheng, Y., Li, Q., & Tong, R. K. (2025). GM-ABS: Promptable Generalist Model Drives Active Barely Supervised Training in Specialist Model for 3D Medical Image Segmentation. IEEE Transactions on Medical Imaging. https://ieeexplore.ieee.org/abstract/document/11119675/

Yie, S. Y., Yoon, S., Yang, J., Kim, K., Lee, J. S., & Li, Q. (2025). Score-based-diffusion-model-based synthetic CT generation from MR images and post-hoc uncertainty analysis. Medical Imaging 2025: Image Processing, 13406, 600–604. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13406/134062E/Score-based-diffusion-model-based-synthetic-CT-generation-from-MR/10.1117/12.3047308.short

Yoon, S., Guo, N., & Li, Q. (2025). Three-Dimensional 18-FDG PET/CT Synthesis from Demographic Data Using Cascaded Diffusion Models. Society of Nuclear Medicine. https://jnm.snmjournals.org/content/66/supplement_1/251300.abstract

Yoon, S., Oh, Y., Jin, P., Song, S., Tivnan, M., Wu, D., Li, X., & Li, Q. (2025). Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface (No. arXiv:2505.22511). arXiv. https://doi.org/10.48550/arXiv.2505.22511

Yoon, S., Oh, Y., Li, X., Xin, Y., Cereda, M., & Li, Q. (2025). High-fidelity 3D lung CT synthesis in ARDS swine models using score-based 3D residual diffusion models. Medical Imaging 2025: Imaging Informatics, 13411, 160–164. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13411/134110R/High-fidelity-3D-lung-CT-synthesis-in-ARDS-swine-models/10.1117/12.3047621.short

Yoon, S., Oh, Y., Tivnan, M., Song, S., Jin, P., Kim, S., Cho, H. J., Wu, D., Uppot, R., & Li, Q. (2025). Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model (No. arXiv:2503.04966). arXiv. https://doi.org/10.48550/arXiv.2503.04966

Yoon, S., Song, S., Jin, P., Tivnan, M., Oh, Y., Kim, S., Wu, D., Li, X., & Li, Q. (2026). Cascaded 3D Diffusion Models for Whole-Body 3D 18-F FDG PET/CT Synthesis from Demographics. In J. C. Gee, D. C. Alexander, J. Hong, J. E. Iglesias, C. H. Sudre, A. Venkataraman, P. Golland, J. H. Kim, & J. Park (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2025 (Vol. 15962, pp. 99–109). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04947-6_10

Yuan, M., Jin, P., Li, N., & Li, Q. (2025). PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models (No. arXiv:2509.20570). arXiv. https://doi.org/10.48550/arXiv.2509.20570

2024

Płotka S, Szczepański T, Szenejko P, Korzeniowski P, Calvo JR, Khalil A, Shamshirsaz A, Brawura-Biskupski-Samaha R, Išgum I, Sánchez CI, Sitek A. Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome. Medical Image Analysis. 2025 Jan 1;99:103330.

Park SJ, Park J, Min J, Wu D, Kim D, Kang K, Lee D, Gupta R, Jung J, Han K. Mobile photon counting detector CT with multi material decomposition methods for neuroimaging of patients in intensive care unit. Scientific Reports. 2024 Dec 30;14(1):31745.

Chen Z, Yoon S, Strotzer Q, Khalid RN, Tivnan M, Li Q, Gupta R, Wu D**. Portable Head CT Motion Artifact Correction via Diffusion-based Generative Model. Computerized Medical Imaging and Graphics. 2024 Dec 13:102478.

Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Yeona Cho, Ik Jae Lee, Jin Sung Kim, and Jong Chul Ye, “LLM-driven Multimodal Target Volume Contouring in Radiation Oncology“, Nature Communications 15 (1), 9186, 2024.

Chen Z, Yoon S, Tivnan M, Hu R, Li Q, Wu D**. Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models. The Third Workshop on Application of Medical Artificial Intelligence – AMAI 2024.

Grzeszczyk MK, Korzeniowski P, Alabed S, Swift AJ, Trzciński T, Sitek A. TabMixer: Noninvasive Estimation of the Mean Pulmonary Artery Pressure via Imaging and Tabular Data Mixing. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2024 Oct 4 (pp. 670-680). Cham: Springer Nature Switzerland.Goog

Yoon S, Tivnan M, Hu R, Wang Y, Son YD, Wu D, Li X, Kim K, Li Q. Volumetric Conditional Score-Based Residual Diffusion Model for PET/MR Denoising. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2024 Oct 3 (pp. 754-763). Cham: Springer Nature Switzerland.

 Tivnan M, Yoon S, Chen Z, Li X, Wu D, Li Q. Hallucination Index: An Image Quality Metric for Generative Reconstruction Models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2024 Oct 3 (pp. 449-458). Cham: Springer Nature Switzerland.

Szczepański T, Grzeszczyk MK, Płotka S, Adamowicz A, Fudalej P, Korzeniowski P, Trzciński T, Sitek A. Let Me DeCode You: Decoder Conditioning with Tabular Data. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2024 Oct 3 (pp. 228-238). Cham: Springer Nature Switzerland.

Yujin Oh, Sangjoon Park, Xiang Li, Wang Yi, Jonathan Paly, Jason Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho, Chan Woo Wee, Peng Shu, Peilong Wang, Nathan Yu, Jason Holmes, Jong Chul Ye, Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim, “Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation”, arxiv.org/abs/2410.00046, 2024.

Kwanyoung Kim, Yujin Oh, and Jong Chul Ye, “OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation”, in Proceedings of 2024 European Conference on Computer Vision (ECCV).

Renc P, Jia Y, Samir AE, Was J, Li Q, Bates DW, Sitek A. Zero shot health trajectory prediction using transformer. NPJ Digital Medicine. 2024 Sep 19;7(1):256.

Chen, Wenting, et al. “Medical image synthesis via fine-grained image-text alignment and anatomy-pathology prompting.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024.

Xu, Zhe, Cheng Chen, Donghuan Lu, Jinghan Sun, Dong Wei, Yefeng Zheng, Quanzheng Li, and Raymond Kai-yu Tong. “FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 294-304. Cham: Springer Nature Switzerland, 2024.

Chen C, Miao J, Wu D, Zhong A, Yan Z, Kim S, Hu J, Liu Z, Sun L, Li X, Liu T. Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation. Medical Image Analysis. 2024 Aug 22:103310.

Chen Z, Tivnan M, Yoon S, Hu R, Li Q, Wu D*. Optimizing Conditional DDPM for Head CT Motion Artifact Reduction: Brain vs. Skull and 3D vs. 2D. In: The 8th International Conference on Image Formation in X-Ray Computed Tomography. Bamberg, Germany; August 5 – 9, 2024 (poster presentation)

Wang Y, Yoon S, Hu R, Yu B, Lee D, Gupta R, Zhang L, Chen Z, Wu D**. Noise Controlled CT Super-Resolution with Conditional Diffusion Model. In: The 8th International Conference on Image Formation in X-Ray Computed Tomography. Bamberg, Germany; August 5 – 9, 2024. (poster presentation)

Feng Y, Sitek A, Shadi AE, Sabet H. Collimator-less SPECT System Design for Dynamic Whole-body Imaging. arXiv preprint arXiv:2406.05220. 2024 Jun 7.

Hu R, Wu D, Tivnan M, Guo N, Yoon S, Chen Z, Wang Y, Luo J, Xue S, Cui J, Liu H. Unsupervised low-dose PET image reconstruction based on pre-trained denoising diffusion probabilistic prior. Journal of Nuclear Medicine June 2024, 65 (supplement 2) 241109. (poster presentation)

Hu R, Yoon S, Wu D, Tivnan M, Chen Z, Wang Y, Luo J, Cui J, Li Q, Liu H, Guo N. Realistic Tumor generation using 3D conditional latent diffusion model. Journal of Nuclear Medicine June 2024, 65 (supplement 2) 241725. (oral presentation)

Chen Z, Li Q, Wu D**. Estimate and Compensate Head Motion in Non-contrast Head CT Scans Using Partial Angle Reconstruction and Deep Learning. Medical Physics. 2024; 51: 3309 – 3321.

 Oh J, Wu D, Hong B, Lee D, Kang M, Li Q, Kim K. Texture-preserving low dose CT image denoising using Pearson divergence. Physics in Medicine & Biology. 2024 May 21;69(11):115021.

Yan Z, Zhang L, Li Q, Wu D**. Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks. In: Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications. SPIE; 2024. (oral presentation).

Chen, Wenting, et al. “Fine-grained image-text alignment in medical imaging enables explainable cyclic image-report generation.” Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Grzeszczyk MK, Trzciński T, Sitek A. MISS: Multiclass Interpretable Scoring Systems. InProceedings of the 2024 SIAM International Conference on Data Mining (SDM) 2024 (pp. 55-63). Society for Industrial and Applied Mathematics.

Näppi JJ, Hironaka T, Wu D, Gupta R, Tachibana R, Taguchi K, Okamoto M, Yoshida H. Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography. In Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications 2024 Apr 2 (Vol. 12931, pp. 305-310). SPIE. (poster presentation)

Hariri M, Chirra P, Patel M, Einat TT, Dayan I, Tonetti A, Baror Y, Barrett T, Sushentsev N, Kaggie J D, Yuan S, Wu D, Yu B, Lyu Z, Hsu C, Wang W, Krishnamurthi S, Viswanath SE. Federated image quality assessment of prostate MRI scans in a multi-institutional setting. Cancer Research. 2024 Mar 22;84(6_Supplement):2344. (poster presentation)

Sitek A. Artificial Intelligence in Radiology: Bridging Global Health Care Gaps through Innovation and Inclusion. Radiology: Artificial Intelligence. 2024 Mar 13;6(2):e240093.

Jarraya M, Bitoun O, Wu D, Balza R, Guermazi A, Collins J, Gupta R, Nielsen GP, Guermazi E, Simeone FJ, Omoumi P. Dual energy computed tomography cannot effectively differentiate between calcium pyrophosphate and basic calcium phosphate diseases in the clinical setting. Osteoarthritis and Cartilage Open. 2024 Mar 1;6(1):100436.

Payette K, Steger C, Licandro R, de Dumast P, Li HB, Barkovich M, Li L, Dannecker M, Chen C, Ouyang C, McConnell N. Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results. arXiv preprint arXiv:2402.09463. 2024 Feb 8.

Bano S, Casella A, Vasconcelos F, Qayyum A, Benzinou A, Mazher M, Meriaudeau F, Lena C, Cintorrino IA, De Paolis GR, Biagioli J. Placental vessel segmentation and registration in fetoscopy: literature review and MICCAI FetReg2021 challenge findings. Medical Image Analysis. 2024 Feb 1;92:103066.

Grzeszczyk MK, Adamczyk P, Marek S, Pręcikowski R, Kuś M, Lelujko MP, Blanco R, Trzciński T, Sitek A, Malawski M, Lisowska A. Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load. InAMIA Annual Symposium Proceedings 2024 Jan 11 (Vol. 2023, p. 389).

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.