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Publications

AI in Brain Disease Modeling

  1. Zhang, L., Na, S., Liu, T., Zhu, D. and Huang, J. (2023). Multimodal Deep Fusion in Hyperbolic Space for Mild Cognitive Impairment Study. In the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). (Early Accepted, Rate: 13.6%; NIH-MICCAI STudent-Author Registration (STAR) Award; Oral)
  2. Zhang, L., Yu, X., Lyu, Y., Liu, T. and Zhu, D. (2023). Representative Functional Connectivity Learning for Multiple Clinical Groups in Alzheimer’s Disease. In IEEE 20th International Symposium on Biomedical Imaging (ISBI).
  3. Zhang, L. , Wang, L., Liu, T., and Zhu, D. (2023). Disease2Vec: Representing Alzheimer’s Progression via Disease Embedding Tree. Pharmacological Research. (IF: 9.3)
  4. Zhang, L.☨, Qu, J., Ma, H., Chen, T., Liu, T., and Zhu, D. (2023). Exploring Alzheimer’s Disease: A Comprehensive Brain Connectome-Based Survey. Psychoradiology. ☨ Corresponding Author.
  5. Zhang, L., Wang, L., Gao, J., Risacher, S.L., Yan, J., Li, G., Liu, T. and Zhu, D. (2021). Deep fusion of brain structure-function in mild cognitive impairment. Medical Image Analysis (MedIA). (IF: 13.828)
  6. Zhang, L., Wang, L. and Zhu, D., (2020). Jointly Analyzing Alzheimer's Disease Related Structure-Function Using Deep Cross-Model Attention Network. In IEEE 17th International Symposium on Biomedical Imaging (ISBI). (Oral)
  7. Zhang, L., Zaman, A., Wang, L., Yan, J. and Zhu, D. (2019). A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment. In International Workshop on Machine Learning in Medical Imaging (MLMI).
  8. Yu, X., Scheel, N., Zhang, L., Zhu, D.C., Zhang, R. and Zhu, D., (2021). Free water in T2 FLAIR white matter hyperintensity lesions. Alzheimer's \& Dementia.
  9. Wang, L., Zhang, L., and Zhu, D., (2020). Learning Latent Structure Over Deep Fusion Model of Mild Cognitive Impairment. In IEEE 17th International Symposium on Biomedical Imaging (ISBI).
  10. Wang, L., Zhang, L., and Zhu, D., (2019). Accessing Latent Connectome of Mild Cognitive Impairment via Discriminant Structure Learning. In IEEE 16th International Symposium on Biomedical Imaging (ISBI).

AI in Brain Fundamental Organization Principles

  1. Zhang, L., Wu, Z., Yu, X., Lyu, Y., Dai, H., Zhao, L., Wang, L., Li, G., Wang, X., Liu, T.*, and Zhu, D.* (2023). Learning Lifespan Brain Anatomical Correspondence via Cortical Developmental Continuity Transfer. IEEE Transactions on Neural Networks and Learning Systems (TNNLS). (IF: 14.255) * Co-corresponding authors. (Under Review)
  2. Zhang, L., Wang, L. and Zhu, D. (2022). Predicting brain structural network using functional connectivity. Medical Image Analysis (MedIA). (IF: 13.828)
  3. Zhang, L., Zhao, L., Liu, D., Wu, Z., Wang, X., Liu, T. and Zhu, D. (2022). Cortex2vector: Anatomical Embedding of Cortical Folding Patterns. Cerebral Cortex. (IF: 5.998)
  4. Zhang, L., Wang, L. and Zhu, D., (2020). Recovering brain structural connectivity from functional connectivity via multi-GCN based generative adversarial network. In the 23rd International Conference on Medical mage Computing and Computer-Assisted Intervention (MICCAI). (Early Accepted, Rate: 13.3%; Prestigious Young Scientist Award (Best Paper Award), Rate: 4/1809 =0.2%; Oral)
  5. Zhang, S., Zhang, T., He, Z., Li, X., Zhang, L., Zhu, D., Jiang, X., Liu, T., Han, J. and Guo, L., (2023). Gyral peaks and patterns in human brains. Cerebral Cortex. (IF: 5.998)
  6. Gao, X., Zhang, X., Zhang, L., Xu, X. and Zhu, D. (2023). Predicting Diverse Functional Connectivity from Structural Connectivity Based on Multi-contexts Discriminator GAN. In the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). (Early Accepted, Rate: 13.3%)
  7. Yu, X., Hu, D., Zhang, L., Huang, Y., Wu, Z., Liu, T., Wang, L., Lin, W., Zhu, D., and Li. G. (2022). Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network. In the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
  8. Zaman, A., Zhang, L., Yan, J. and Zhu, D. (2019). Multi-modal image prediction via spatial hybrid U-Net. In the Multiscale Multimodal Medical Imaging (MMMI). ( Best Oral Paper Award, Rate: 10%; Oral)

Brain Inspired AI

  1. Zhao, L.*, Zhang, L.*, Wu, Z., Chen, Y., Dai, H., Yu, X., Liu, Z., Zhang, T., Hu, X., Jiang, X. and Li, X. (2023). When brain-inspired ai meets agi. Meta-Radiology. * co-first authors
  2. Yu, X.*, Zhang, L.*, Dai, H., Zhao, L., Lyu, Y., Liu, T. and Zhu, D., (2023). Core-Periphery Principle Guided Redesign of Self-Attention in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). * co-first authors. (IF: 24.314). (Under Review)
  3. Yu, X., Zhang, L., Zhu, D. and Liu, T. (2023). Robust Core-Periphery Constrained Transformer for Domain Adaptation. arXiv preprint arXiv:2308.13515.
  4. Chen, Y., Xiao, Z., Du, Y., Zhao, L., Zhang, L., Wu, Z., Liu, D., Zhu, D., Zhang, T., Hu, X., Liu, T., and Jiang, X., (2023). A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers. IEEE Transactions on Neural Networks and Learning Systems (TNNLS).(IF: 14.255)
  5. Zhao, L., Dai, H., Wu, Z., Xiao, Z., Zhang, L., Liu, D.W., Hu, X., Jiang, X., Li, S., Zhu, D. and Liu, T. (2023). Coupling visual semantics of artificial neural networks and human brain function via synchronized activations. IEEE Transactions on Cognitive and Developmental Systems (TCDS).(IF: 4.546)
  6. Huang, H., Zhao, L., Hu, X., Dai, H., Zhang, L.*, Zhu, D. and Liu, T. (2023). BI AVAN: Brain inspired adversarial visual attention network. IEEE Transaction on Multimedia.(IF: 8.182) (Under Review)

Large Foundation Model/Large Language Model

  1. Wu, Z.*, Zhang, L.*, Cao, C.*, Yu, X., Dai, H., Ma, C., Liu, Z., Zhao, L., Li, G., Liu, W. and Li, Q., (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. * co-first authors. (Citation: 32)
  2. Li, X.*, Zhao, L.*, Zhang, L.*, Wu, Z., Liu, Z., X. S., Yuan, Y., Liu, J., Li, G., Zhu, D., Yan, P., Li, Q., and Liu, W. (2023). Artificial General Intelligence for Medical Imaging. arXiv preprint arXiv:2306.05480. * co-first authors. (Citation: 20)
  3. Liu, Z.*, Yu, X.*, Zhang, L.*, Wu, Z., Cao, C., Dai, H., Zhao, L., Liu, W., Shen, D., Li, Q. and Liu, T. (2023). Deid-gpt: Zero-shot medical text de-identification by gpt-4. arXiv preprint arXiv:2303.11032. * co-first authors. (Citation: 98)
  4. Liu, Z., Zhang, L., Wu, Z., Yu, X., Cao, C., Dai, H., Liu, N., Liu, J., Liu, W., Li, Q. and Shen, D. (2023). Surviving ChatGPT in Healthcare. Frontiers in Radiology.
  5. Xiao, Z., Chen, Y., Yao, J., Zhang, L., Wu, Z., Yu, X., Pan, Y., Zhao, L., Ma, C., Liu, X. and Liu, W. (2023). Instruction-vit: Multi-modal prompts for instruction learning in vit. Information Fusion. ((IF: 18.6))
  6. Zhang, L., Liu, Z., Zhang, L., Wu, Z., Yu, X., Holmes, J., Feng, H., Dai, H., Li, X., Li, Q. and Zhu, D. (2023). Segment Anything Model (SAM) for Radiation Oncology. arXiv preprint arXiv:2306.11730. (Citation: 18)
  7. Liu, Z., Zhong, T., Li, Y., Zhang, Y., Pan, Y., Zhao, Z., Dong, P., Cao, C., Liu, Y., Shu, P., Wei, Y., Wu, Z., Ma, C., Wang, J., Wang, S., Zhou, M., Jiang, Z., Li, C., Holmes, J., Xu, S., Zhang, L., Dai, H., Zhang, K., Zhao, L., Chen, Y., Liu, X., Wang, P., Yan, P., Liu, J., Ge, B., Sun, L., Zhu, D., Li, X., Liu, W., Cai, X., Hu, X., Jiang, X., Zhang, S., Zhang, X., Zhang, T., Zhao, S., Li, Q., Zhu, H., Shen, D., and Liu, T. (2023). Evaluating large language models for radiology natural language processing. arXiv preprint arXiv:2307.13693. (Citation: 11)
  8. Liu, C., Liu, Z., Holmes, J., Zhang, L., Zhang, L., Ding, Y., Shu, P., Wu, Z., Dai, H., Li, Y. and Shen, D. (2023). Artificial General Intelligence for Radiation Oncology. arXiv preprint arXiv:2309.02590.(Citation: 9)