代表性论著：[1] Li, Z., Zheng, Y., Shan, D., Yang, S., Li, Q., Wang, B., Zhang, Y., Hong, Q. Q.*, Shen, D.* (2024), ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. IEEE Transactions on Medical Imaging. (中科院1区)
[2] Hong, Q. Q. *, et al. (2024), NeuFG: Neural Fuzzy Geometric Representation for 3D Reconstruction, IEEE Transactions on Fuzzy Systems. (中科院1区, In Press)
[3] Hong, Q. Q., Lin, L., Li, Z., Li, Q., Yao, J., Wu, Q.*, Liu, K.*, Tian, J. (2024), A distance transformation deep forest framework with hybrid-feature fusion for CXR image classification, IEEE Transactions on Neural Networks and Learning Systems. (中国科学院1区, In Press)
[3] Li. Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., Jin, D., Hong, Q. Q.* (2024), LViT: language meets vision transformer in medical image segmentation. IEEE Transactions on Medical Imaging, vol. 43, no. 1, pp. 96-107, 2024. (中国科学院1区)
[4] Li, Z., Zheng, Y., Luo, X., Shan, D., and Hong, Q. Q.* (2023), ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON, Canada. (CCF A)
[5] Qiu, Y., Li, Z., Wang, Y., Dong, P., Wu, D., Yang, X., Hong, Q. Q.*, Shen, D.* (2023), CorSegRec: A Topology-Preserving Scheme for Extracting Fully-Connected Coronary Arteries from CT Angiography, MICCAI 2023, Vancouver, Canada, October 8-12, 2023. (CCF B, Oral, 最佳论文提名)
[6] Yang, C., Chen, J., Li, K., and Hong, Q. Q.* (2024), FFnsr: Fast and Fine Neural Surface Reconstruction, IEEE International Conference on Multimedia and Expo (ICME2024). (CCF B, Accept)
[7] Lin, Q., Xie, W., Zhou, R., Cao, X., Chen, J., Yao, J.*, and Hong, Q. Q.* (2024), DPP-Net: Difficulty Perception-Processing Heterogeneous Network for Semi-supervised Medical Image Segmentation, IEEE International Conference on Multimedia and Expo (ICME2024). (CCF B, Accept)
[8] Cao, X., Xie, W., Cao, X., Zhou, R., Lin, Q., Yao, J.*, and Hong, Q. Q.* (2024), ICR-Net: Semi-supervised Medical Image Segmentation Guided by Intra-sample Cross Reconstruction, IEEE International Conference on Multimedia and Expo (ICME2024). (CCF B, Accept)
[9] Shan, D., Li, Z., Chen, W., Li, Q., Tian, J., Hong, Q. Q.* (2023), Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023). (CCF B)
[10] Zhou, R., Yao, J.*, Hong, Q. Q. *, Zheng, Y., Zheng, L. (2023), DAMS-Net: Dual attention and multi-scale information fusion network for 12-lead ECG classification. Methods, 2023. (中国科学院3区)
[11] Zhang, W., Su, S.*, Hong, Q. Q. *, Wang, B., Li, S. (2023), Long short-distance topology modelling of 3D point cloud segmentation with a graph convolution neural network. IET Comput. Vis., 1– 14, 2023. (CCF C)
[12] Zhou, R., Yao, J.*, Hong, Q. Q. *, Li, X., Cao, X. (2023), Cross Attention Multi Scale CNN-Transformer Hybrid Encoder Is General Medical Image Learner. PRCV 2023. (CCF C)
[13] Lin, Q., Yao, J.*, Hong, Q. Q. *, Cao, X., Zhou, R., Xie, W. (2023), LATrans-Unet: Improving CNN-Transformer with Location Adaptive for Medical Image Segmentation. PRCV 2023. (CCF C)
[14] Li, Y., Zong, Y., Sun, W., Wu, Q.*, Hong, Q. Q. *(2023), A Long-Tail Relation Extraction Model Based on Dependency Path and Relation Graph Embedding, 7th APWeb-WAIM International Joint Conference on Web and Big Data, Oct 06, 2023 - Oct 08, 2023. (CCF C)
[15] Cao, X., Yao, J.*, Hong, Q. Q., Zhou, R. (2023), MEA-TransUNet: A Multiple External Attention Network for Multi-Organ Segmentation. ICCAN 2023. (CCF C)
[16] Li, X., Song, S., Yao, J.*, Zhang, H., Zhou, Z., and Hong, Q. Q., Efficient Collision Detection using Hybrid Medial Axis Transform and BVH for Rigid Body Simulation, Graphical Models, 2023. (CCF B)
[17] Xu, F.#, Lin, L.#, Li, Z., Hong, Q. Q. * , Liu, K.*, Li, Q., Zheng, Y., Tian, J. (2022), MRDFF: A deep forest based framework for CT whole heart segmentation, Methods, 2022. (中国科学院3区)
[18] Li, Z.#, Li, D.#, Wang, W., Hong, Q. Q*, Li, Q., Tian, J. (2022), TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation, The 31st International Conference on Artificial Neural Networks (ICANN2022). (CCF C)
[19] Liu, K., Zhan, P., Liang, Y., Zhang, Y., Guo, H., Yao, J., Wu, Q.*, Hong, Q. Q. * (2022). The design of error-correcting output codes algorithm for the open-set recognition. Applied Intelligence, 52, pp. 7843–7869, 2022. (中国科学院2区)
[20] Hong, Q., Lin, L., Li, Q., Jiang, Z., Fang, J., Wang, B., Liu, K. & Huang, C. (2021). A direct slicing technique for the 3D printing of implicitly represented medical models. Computers in Biology and Medicine, 135, 104534. (中国科学院2区)
[21] Fei Xu#, Lingli Lin#, Dihan Li, Qingqi Hong*, Kunhong Liu*,Qingqiang Wu, Qingde Li, Yinhuan Zheng, Jie Tian (2021). A Multi-Resolution Deep Forest Framework with Hybrid Feature Fusion for CT Whole Heart Segmentation, IEEE BIBM 2021. (CCF B)
[22] Xiang Yu, Jian Wang, Qingqi Hong*, Raja Teku, Shui-Hua Wang*, Yudong Zhang* (2022). Transfer learning for medical images analyses: A survey, Neurocomputing. (中国科学院2区)
[23] Liu, K., Ye, X., Guo, H., Wu, Q.*, Hong, Q. Q.. The design of soft recoding-based strategies for improving error-correcting output codes. Applied Intelligence, 52, pp. 8856–8873, 2022. (中国科学院2区)
[24] Hong, Q., Ding, Y., Lin, J., et al. Image-Based Automatic Watermeter Reading under Challenging Environments. Sensors, 2021, 21(2): 434. (SCI)
[25] Gao, J., Liu, K., Wang, B., Wang, D., & Hong, Q. (2021). An improved deep forest for alleviating the data imbalance problem. Soft Computing, 25(3), 2085-2101. (CCF C)
[26] Hong, Q., Li, Q., Wang, B., Tian, J., Xu, F., Liu, K., & Cheng, X. (2020). High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations. Computer Methods and Programs in Biomedicine, 196, 105598. (中国科学院2区)
[27] Zhang, W., Su, S., Wang, B., Hong, Q., & Sun, L. (2020). Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487-498. (中国科学院2区)
[28] Huang, C., Lan, Y., Xu, G., Zhai, X., Wu, J., Lin, F., Zeng, N., Hong, Q.,…, & Zhang, G. (2021). A deep segmentation network of multi-scale feature fusion based on attention mechanism for IVOCT lumen contour. IEEE/ACM Transactions on computational biology and bioinformatics, 18(1), 62-69. (CCF B)
[29] Sun, M., Liu, K., Wu, Q., Hong, Q., Wang, B., & Zhang, H. (2019). A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis. Pattern Recognition, 90, 346-362. (中国科学院1区)
[30] Wu, Q., Kuang, Y., Hong, Q., & She, Y. (2019). Frontier knowledge discovery and visualization in cancer field based on KOS and LDA. Scientometrics, 118(3), 979-1010. (中国科学院2区)
[31] Hong Q, Li Q, Wang B, Liu K*, Qi Q, 2019. High precision implicit modeling for patient-specific coronary arteries, IEEE access. 7: 72020-72029. (中国科学院3区)
[32] Hong, Q. Q., Q. Li, B. Wang, K. Liu, F. Lin, J. Lin, and et al. Accurate geometry modeling of vasculatures using implicit fitting with 2d radial basis functions. Computer Aided Geometric Design, 62:206–216, 2018. (CCF B)
[33] Hong, Q. Q., Yan Li, Q. Li, B. Wang, J. Yao, Q. Wu, Y. She, An implicit skeleton-based method for the geometry reconstruction of vasculatures, The Visual Computer. Vol. 32, Issue 10, pp. 1251–1262, 2016. (SCI, CCF C)
[34] Hong, Q. Q., Wang, B., Li, Q., Li, Y. and Wu, Q., GPU Accelerating Technique for Rendering Implicitly Represented Vasculatures, Bio-Medical Materials and Engineering, vol. 14, no 1, pp. 1351-1357, 2014. (SCI)
[35] Zou, Q., Li, J., Hong, Q. Q.*, Lin, Z., Wu, Y., Shi, H., and Ju, Y., Prediction of microRNA-disease associations based on social network analysis methods, BioMed research international, vol. 2015, 2015. (SCI)
[36] Wang, B., Ge, Q., Hong, Q. Q.*, Li, Y., Liu, K., and Jiang, Z., Vascular Model Editing for 3D Printing Based on Implicit Functions, In proceedings of 14th Chinese Conference on Image and Graphics Technologies, pp. 150-160, 2019.
[37] Lu, S., Chen, H., Zhou, X., Wang, B., Wang, H., and Hong, Q. Q., Graph-Based Collaborative Filtering with MLP, Mathematical Problems in Engineering, vol. 2018, 2018. (SCI)
[38] Liu, K.-H., Ng, V. T. Y., Liong, S.-T., and Hong, Q. Q., Microarray Data Classification Based on Computational Verb, IEEE Access, vol. 7, pp. 103310-103324, 2019. (中国科学院3区)
[39] Li, Q., Hong, Q. Q., Qi, Q., Ma, X., Han, X., and Tian, J., Towards additive manufacturing oriented geometric modeling using implicit functions, Visual Computing for Industry, Biomedicine, and Art, vol. 1, no. 1, pp. 1-16, 2018.
[40] Wu, Q., Zhang, C., Hong, Q. Q., and Chen, L., Topic evolution based on LDA and HMM and its application in stem cell research, Journal of Information Science, vol. 40, no. 5, pp. 611-620, 2014. (中国科学院3区)
[41] Quan, Q., Qingde, L., and Hong, Q. Q., Skeleton marching: A high-performance parallel vascular geometry reconstruction technique, 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-6.
[42] Han, B., Zhang, Z., Xu, C., Wang, B., Hu, G., Bai, L., Hong, Q. Q., and Hancock, E. R., Deep face model compression using entropy-based filter selection, In proceedings of International Conference on Image Analysis and Processing, pp. 127-136, 2017.
[43] 郑银环, 王备战, 王嘉珺, 陈凌宇, 洪清启, 深度卷积神经网络应用于人脸特征点检测研究, 计算机工程与应用, (4): 173-178, 2019.
[44] Li, H., Zhao, J., Lin, D., Su, J., Wu, Q., and Hong, Q. Q.*, The Research and Application of Reservoir Identification Model Based on Smap-ED, International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 12, pp. 255-264, 2015.
[45] Hong, Q. Q. *, A skeleton-based technique for modeling implicit surfaces, In proceedings of 6th International Congress on Image and Signal Processing, pp. 686-691, Hangzhou, China, December, 2013.
[46] Hong, Q. Q. and Wang, B., Segmentation of vessel images using a localized hybrid level-set method, In proceedings of 6th International Congress on Image and Signal Processing, pp. 631 – 635, Hangzhou, China, December, 2013.
[47] Hong, Q. Q.*, Chen, L., Wang, B., and Wu, Q., The Extraction of Vascular Axis Based on Signed Distance Function, In proceedings of 5th International Conference on Graphic and Image Processing, Hong Kong, October, 25-27, 2013.
[48] Hong, Q. Q.*, Li, Q., and Tian, J., Local Hybrid Level-set Method for MRA Image Segmentation, In proceedings of 10th IEEE International Conference on Computer and Information Technology, pp. 1397 - 1402, Braford, UK, June, 2010.
[49] Hong, Q. Q.*, Li, Q., and Tian, J., Virtual Angioscopy based on implicit vasculatures, Lecture Note in Computer Science (LNCS), vol. 6785, pp. 592-603, 2011.
[50] Hong, Q. Q.*, Li, Q., and Tian, J., Implicit reconstruction of vasculatures using bivariate piecewise algebraic splines, IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp. 543-553, 2012. (中国科学院1区)
