Conferences

2022

Xiyue Wang, Jinxi Xiang, Jun Zhang*, Sen Yang, Zhongyi Yang, Ming-Hui Wang, Jing Zhang*, Yang Wei, Junzhou Huang, Xiao Han.SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification. NeurIPS 2022. (*Co-corresponding Author)

Yonghang Guan*, Jun Zhang*, Kuan Tian, Sen Yang, Pei Dong, Jinxi Xiang, Wei Yang, Junzhou Huang, Yuyao Zhang, Xiao Han,  Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification CVPR2022. Oral (*Co-first Author)

2021

  1. Zhen Chen*, Jun. Zhang*, Shuanlong Che, Junzhou Huang, Xiao Han, Yixuan Yuan, Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring, AAAI 2021. (*Co-first Author)
  2. Siqi Yang*, Jun. Zhang*, Junzhou Huang, B. Lovell, Xiao Han,  Minimizing Labeling Cost for Nuclei Instance Segmentation and Classificationwith Cross-domain Images and Weak Labels, AAAI 2021. (*Co-first Author)
  3. Wang Y., Yang S., Zhang J., Yang W., Huang J., Han X.  TransPath: Transformer-based Self-supervised Learning for Histopathological Image Classification, MICCAI 2021
  4. Li H., Yang F., Xing X., Zhao Y., Zhang J., Liu Y., Han M., Huang J., Wang L., Yao J.  Multi-modal Multi-instance Learning using Weakly Correlated Histopathological Images and Tabular Clinical Information, MICCAI 2021
  5. Li H., Yang F., Zhao Y., Xing X., Zhang J., Gao M., Huang J., Wang L., Yao J. (2021) DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image, MICCAI 2021

2020

  1. Kuan Tian*, Jun Zhang*, Haocheng Shen, Kezhou Yan, Pei Dong,  Jianhua Yao, Shannon Che, Pifu Luo, Xiao Han, Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy. MICCAI 2020. (*Co-first Author)
  2. Donglin Di, Shengrui Li, Jun Zhang, Yue Gao, Ranking-Based Survival Prediction on Histopathological Whole-Slide Images. MICCAI 2020.
  3. Haocheng Shen, Kuan Tian, Pei Dong, Jun Zhang, Kezhou Yan, Shannon
    Che, Jianhua Yao, Pifu Luo, Xiao Han, Deep Active Learning for Breast Cancer
    Segmentation in Immunohistochemistry Images. MICCAI 2020.
  4. Chuang Niu, Jun Zhang, Ge Wang, Jimin Liang, Self-Supervised Gaussian-Attention Network for Image Clustering, ECCV 2020.
  5. Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang, Jiarui Sun, Sen Yang, Bjoern Menze, Xinjuan Fan and Jianhua Yao, Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning with Deep Graph Convolution. CVPR 2020.

2018

  1. Jun Zhang, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski, Breast Tumor Segmentation in DCE-MRI Using Fully Convolutional Networks with an Application in Radiogenomics. SPIE Medical Imaging, 2018. (Oral Presentation)
  2. Jun Zhang, Elizabeth Hope Cain, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski, Breast Mass Detection in Mammography and Tomosynthesis via Fully Convolutional Network-based Heatmap Regression. SPIE Medical Imaging, 2018. (Oral Presentation)
  3. Jun Zhang, Sujata V. Ghate, Lars J. Grimm, Ashirbani Saha, Elizabeth Hope Cain, Zhe Zhu, Maciej A. Mazurowski, Convolutional Encoder-Decoder for Breast Mass Segmentation in Digital Breast Tomosynthesis. SPIE Medical Imaging, 2018.
  4. Mingxia Liu, Jun Zhang, Chunfeng Lian, Dinggang Shen. Context­ Guided Deep Learning Framework for Skull Stripping. The 104th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA2018), Chicago, USA, Nov. 25-30, 2018. (Abstract)
  5. Zhe Zhu, Michael R. Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, Shelley Hwang, Maciej A. Mazurowski, Deep Learning-based Features of Breast MRI for Prediction of Occult Invasive Disease Following a Diagnosis of Ductal Carcinoma in Situ: Preliminary Data. SPIE Medical Imaging, 2018.
  6. Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, Maciej A. Mazurowski. Breast Cancer Molecular Subtype Classification using Deep Features: Preliminary Results. SPIE Medical Imaging, 2018.
  7. Chunfeng Lian, Mingxia Liu, Jun Zhang, Xiaopeng Zong, Weili Lin, Dinggang Shen. Automatic Segmentation of 3D Perivascular Spaces in 7T MR Images Using Multi-Channel Fully Convolutional Network, Joint Annual Meeting ISMRM-ESMRMB, Paris, France, Jun. 16-21, 2018.  (Power Pitch)

2017

  1. Jun Zhang*, Mingxia Liu*, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo-Fang Shen, Zhen Tang, Ken-Chung Chen, James J. Xia, Dinggang Shen. Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks. The 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec, Canada, Sep. 10-14, 2017. (*Co-first Author)
  2. Mingxia Liu*, Jun Zhang*, Ehsan Adeli, Dinggang Shen. Deep Multi-Task Multi-Channel Learning for Joint Classification and Regression of Brain Status. The 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec, Canada, Sep. 10-14, 2017. (*Co-first Author)
  3. X. Cao, J. Yang, Jun Zhang, D. Nie, M. Kim, Q. Wang, and D. Shen. Deformable image registration based on similarity-steered CNN regression. The 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec, Canada, Sep. 10-14, 2017.
  4. Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, and Dinggang Shen. Efficient Groupwise Registration for Brain MRI by Fast Initialization. In International Workshop on Machine Learning in Medical Imaging, pp. 150-158. Springer, Cham, 2017.
  5. Jun Zhang, Mingxia Liu, Dinggang Shen. Real-time Large-scale Anatomical Landmark Detection with Limited Medical Images. International Society for Magnetic Resonance in Medicine (ISMRM) 25th Annual Meeting and Exhibition, Honolulu, Hawaii, USA, 2017.

2016

  1. Jun Zhang, Yaozong Gao, Sang Hyun Park, Xiaopeng Zong, Weili Lin, Dinggang Shen, Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image. Medical Computer Vision Workshop: Algorithms for Big Data, 2016. 
  2. Jun Zhang, Mingxia Liu, Le An, Dinggang Shen, Landmark-based Alzheimer’s Disease Diagnosis using Longitudinal Structural MR Images. In International Workshop on Machine Learning in Medical Imaging, 2016. (Oral presentation)
  3. Mingxia Liu, Jun Zhang, Pew-Thian Yap,  and Dinggang Shen. Diagnosis of Alzheimer’s Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-Modality Data. In Medical Image Computing and Computer-Assisted Intervention—MICCAI, Springer International Publishing, 2016. (Early Accept; MICCAI Society Young Scientist Award Nomination)
  4. Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, and Dinggang Shen. Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagnosis. In Medical Image Computing and Computer-Assisted Intervention—MICCAI, Springer International Publishing, 2016.
  5. Xiaofeng Zhu, Kim-Han Thung, Jun Zhang, and Dinggang Shen. Fast Neuroimaging-based Retrieval for Alzheimer’s Disease Analysis. In International Workshop on Machine Learning in Medical Imaging, pp. 313-321. Springer International Publishing, 2016.

2015

  1. Jun Zhang, Yaozong Gao, Li Wang, Zhen Tang, James J. Xia, and Dinggang Shen. Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model. In Medical Image Computing and Computer-Assisted Intervention—MICCAI, pp. 661-668. Springer International Publishing, 2015. (Video Download)
  2. Zuoyong Li, Le An, Jun Zhang, Li Wang, James J. Xia, and Dinggang Shen. Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space. In International Workshop on Machine Learning in Medical Imaging, pp. 69-76. Springer International Publishing, 2015.

2014

  1. Jun Zhang, Duofang Chen, Jing Lei, et al., In Vivo Quantitative Bioluminescence Tomography using a Magnetic Resonance Imaging-compatible Optical Imaging System, Proceedings/IEEE International Symposium on Biomedical Imaging-ISBI, 2014, 1 page paper.