Hong-Yu Zhou PhD Candidate Department of Computer Science Advisor: Yizhou Yu Office: Rm 416 (14), Chow Yei Ching Building, HKU Email Address: whuzhouhongyu at gmail dot com Twitter (where I often share some interesting efforts)

Bio

• Now, I am a third-year PhD candidate at Department of Computer Science, The University of Hong Kong. My advisor is Prof. Yizhou Yu.
• My research interests lie primarily in the intersection of artificial intelligence, medicine, and healthcare. The long-term goal is to improve individual healthcare with machine intelligence and medical professionalism.
• At present, I am especially interested in Multi-modal Learning for Biomedicine, where topics that interest me the most involve:
  • **Generalized** Representation Learning across Modalities.
  • **Generic** Algorithms and Models Applied to Varying Modalities.
  • **Knowledge**-enhanced (Large) Language Models Adaption for Biomedical Problems.
• I got my master degree from LAMDA Group at Nanjing University, advised by Prof. Jianxin Wu. Before that, I received my bachelor degree from Wuhan University. I was a senior researcher at Tencent focusing on Medical x AI applications, where my group leader is Dr. Yefeng Zheng.

Recent News

• [2023/04] One paper was accepted by Nature Biomedical Engineering.
• [2023/01] Two papers were accepted by ICLR.
• [2022/12] One paper was accepted by TPAMI.
• [2022/08] One paper was accepted by npj digital medicine.
• [2022/06] One paper was accepted by TPAMI.
• [2022/05] One paper was accepted by MedIA.
• [2022/05] One paper was accepted by MICCAI.
• [2022/04] One paper was accepted by TMI.
• [2022/03] One paper was accepted by CVPR.

Paper (Selected (Highlight) / Full)

2023

A Transformer-based Representation-learning Model with Unified Processing of Multimodal Input for Clinical Diagnostics.
Hong-Yu Zhou*, Yizhou Yu*, Chengdi Wang*, Shu Zhang, Yuanxu Gao, Jia Pan, Jun Shao, Guangming Lu, Kang Zhang, Weimin Li.
[Paper (stay tuned)]
Nature Biomedical Engineering, 2023.

Advancing Radiograph Representation Learning with Masked Record Modeling
Hong-Yu Zhou*, Chenyu Lian*, Liansheng Wang, Yizhou Yu.
[Paper, Code]
International Conference on Learning Representations (ICLR), 2023.

Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling
Hong-Yu Zhou*, Yunxiang Fu*, Zhicheng Zhang, Cheng Bian, Yizhou Yu.
[Paper, Code]
International Conference on Learning Representations (ICLR), 2023.

A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Hong-Yu Zhou*, Chixiang Lu*, Chaoqi Chen, Sibei Yang, Yizhou Yu.
[Paper, Code]
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.

2022

Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases
Chengdi Wang, Jiechao Ma, Shu Zhang, Jun Shao, Yanyan Wang, Hong-Yu Zhou, Lujia Song, Jie Zheng, Yizhou Yu, Weimin Li
[Paper, Code]
npj digital medicine, 2022.

ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification
Zhixiong Yang*, Junwen Pan*, Yanzhan Yang, Xiaozhou Shi, Hong-Yu Zhou, Zhicheng Zhang, Cheng Bian
[Paper, Code]
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022.

Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou, Xiaoguang Han, Yue Huang, Xinghao Ding, Yizhou Yu
[Paper, Code]
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Hong-Yu Zhou*, Chixiang Lu*, Liansheng Wang, Yizhou Yu
[Paper, Code]
IEEE Transactions on Medical Imaging (TMI), 2022.

MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images
Xiaoyu Chen*, Hong-Yu Zhou*, FengLiu, Jiansen Guo, Liansheng Wang, Yizhou Yu
[Paper, Code]
Medical Image Analysis, 2022.

Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang
[Paper, Code]
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2022.

Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
Shu Zhang, Zihao Li, Hong-Yu Zhou, Jiechao Ma, Yizhou Yu.
[Paper, Code]
Technical Report.

Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports
Hong-Yu Zhou*, Xiaoyu Chen*, Yinghao Zhang*, Ruibang Luo, Liansheng Wang, Yizhou Yu.
[Nat. Mach. Intel., Arxiv, Code]
Nature Machine Intelligence, 2022.

2021

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
Hong-Yu Zhou*, Chixiang Lu*, Sibei Yang, Xiaoguang Han, Yizhou Yu.
[Paper, Code]
International Conference on Computer Vision (ICCV), 2021.

nnFormer: Interleaved Transformer for Volumetric Segmentation
Hong-Yu Zhou*, Jiansen Guo*, Yinghao Zhang*, Lequan Yu, Liansheng Wang, Yizhou Yu.
[Paper, Code]
Technical Report.

ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Yizhou Yu.
[Paper]
ICCV workshop on Multi-Task Learning in Computer Vision (DeepMTL).

Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
Bin-Bin Gao, Hong-Yu Zhou.
[Paper, Code]
IEEE Transactions on Image Processing (TIP), 2021.

Generalized Organ Segmentation by Imitating One-Shot Reasoning Using Anatomical Correlation
Hong-Yu Zhou*, Hualuo Liu*, Shilei Cao*, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng.
[Paper]
International Conference on Information Processing in Medical Imaging (IPMI), 2021.

SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Hong-Yu Zhou*, Chengdi Wang*, Haofeng Li*, Gang Wang, Shu Zhang, Weimin Li, Yizhou Yu.
[Paper]
Medical Image Analysis, 2021.

Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge
Liansheng Wang, Cong Xie, Yi Lin, Hong-Yu Zhou, Kailin Chen, Dalong Cheng, Florian Dubost, Benjamin Collery, Bidur Khanal, Bishesh Khanal, Rong Tao, Shangliang Xu, Upasana Upadhyay Bharadwaj, Zhusi Zhong, Jie Li, Shuxin Wang, Shuo Li.
[Paper]
Medical Image Analysis, 2021.

Recist-Net: Lesion Detection via Grouping Keypoints on Recist-Based Annotation
Cong Xie, Shilei Cao, Dong Wei, Hongyu Zhou, Kai Ma, Xianli Zhang, Buyue Qian, Liansheng Wang, Yefeng Zheng.
[Paper]
IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Luyan Liu*^,†, Zhiwei Wen*, Songwei Liu*, Hong-Yu Zhou*^,†, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng.
[Paper, Code]
Technical Report.

Bottom-Up Shift and Reasoning for Referring Image Segmentation
Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu.
[Paper, Code]
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

2020

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng.
[Paper]
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.

Difficulty-aware Glaucoma Classification with Multi-rater Consensus Modeling
Shuang Yu*, Hong-Yu Zhou*, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng.
[Paper]
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations
Hong-Yu Zhou*, Shuang Yu*, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng.
[Paper, Code]
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.

Efficient and Effective Training of Covid-19 Classification Networks with Self-supervised Dual-track Learning to Rank
Yuexiang Li, Dong Wei, Jiawei Chen, Shilei Cao, Hongyu Zhou, Yanchun Zhu, Jianrong Wu, Lan Lan, Wenbo Sun, Tianyi Qian, Kai Ma, Haibo Xu, Yefeng Zheng.
[Paper]
IEEE Journal of Biomedical and Health Informatics (JBHI), 2020.

Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
Bin-Bin Gao, Xin-Xin Liu, Hong-Yu Zhou, Jianxin Wu, Xin Geng.
[Paper]
Technical Report.

2019 and Before

Seg4Reg Networks for Automated Spinal Curvature Estimation
Yi Lin, Hong-Yu Zhou^†, Kai Ma, Xin Yang, Yefeng Zheng.
[Paper]
Ranked 1st place in 2019 MICCAI AASCE Challenge.
Computational Methods and Clinical Applications for Spine Imaging, 2019.

CodeAttention: Translating Source Code to Comments by Exploiting the Code Constructs
Wenhao Zheng, Hongyu Zhou, Ming Li, Jianxin Wu.
[Paper]
Frontiers of Computer Science, 2019.

When Semi-supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Hong-Yu Zhou, Avital Oliver, Jianxin Wu, Yefeng Zheng.
[Paper]
Technical Report.

ThiNet: Pruning CNN Filters for a Thinner Net
Jian-Hao Luo, Hao Zhang, Hong-Yu Zhou, Chen-Wei Xie, Jianxin Wu, Weiyao Lin.
[Paper, Code]
IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2018.

Age Estimation Using Expectation of Label Distribution Learning
Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng.
[Paper, Code]
International Joint Conference on Artificial Intelligence (IJCAI), 2018.

Vortex Pooling: Improving Context Representation in Semantic Segmentation
Chen-Wei Xie, Hong-Yu Zhou, Jianxin Wu.
[Paper, Code]
Technical Report.

Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu.
[Paper]
The British Machine Vision Association (BMVC), 2017.

Adaptive feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu.
[Paper, Code]
International Conference on Computer Vision (ICCV), 2017.

A Transformer-based Representation-learning Model with Unified Processing of Multimodal Input for Clinical Diagnostics.
Hong-Yu Zhou*, Yizhou Yu*, Chengdi Wang*, Shu Zhang, Yuanxu Gao, Jia Pan, Jun Shao, Guangming Lu, Kang Zhang, Weimin Li.
[Paper (stay tuned)]
A multimodal transformer for clinical diagnostics.
Nature Biomedical Engineering, 2023.

Advancing Radiograph Representation Learning with Masked Record Modeling.
Hong-Yu Zhou*, Chenyu Lian*, Liansheng Wang, Yizhou Yu.
[Paper, Code]
Masked + language modeling for visual representation learning.
International Conference on Learning Representations (ICLR), 2023.

Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling.
Hong-Yu Zhou*, Yunxiang Fu*, Zhicheng Zhang, Cheng Bian, Yizhou Yu.
[Paper, Code]
Masked language modeling on knowledge graphs.
International Conference on Learning Representations (ICLR), 2023.

A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis.
Hong-Yu Zhou*, Chixiang Lu*, Chaoqi Chen, Sibei Yang, Yizhou Yu.
[Paper, Code]
New state-of-the-arts of self-supervised learning on 2D and 3D medical data.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.

Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports.
Hong-Yu Zhou*, Xiaoyu Chen*, Yinghao Zhang*, Ruibang Luo, Liansheng Wang, Yizhou Yu.
[Nat. Mach. Intel., Arxiv, Code, AsianScientist, ThePaper(澎湃新闻)]
Nature Machine Intelligence, 2022

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts.
Hong-Yu Zhou*, Chixiang Lu*, Sibei Yang, Xiaoguang Han, Yizhou Yu.
[Paper, Code]
International Conference on Computer Vision (ICCV), 2021.

nnFormer: Interleaved Transformer for Volumetric Segmentation.
Hong-Yu Zhou*, Jiansen Guo*, Yinghao Zhang*, Lequan Yu, Liansheng Wang, Yizhou Yu.
[Paper, Code]
Technical Report.

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations.
Hong-Yu Zhou*, Shuang Yu*, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng.
[Paper, Code]
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.

Selected Awards

• First place in AASCE 2019, held in conjunction with MICCAI 2019. We proposed an approach to accurately estimate the spinal curvature in Adolescent Idiopathic Scoliosis (AIS). Press coverage: 中华网 科技讯 中国好医生网 凤凰网.
• First place in Segthor 2019 Challenge, held in conjunction with ISBI 2019. We proposed a method to segment organs at risk segmentation in Computed Tomography (CT) images. Press coverage: 腾讯科技 机器之心 环球网 量子位 搜狐网.
• First place in AI4Health Challenge, held by Thales group which provides solutions for over 50% of worldwide radiology examinations. In this challenge, we proposed a novel methodology to accurately detect key points in X-rays with a mean error smaller than 0.3 pixels). Press coverage: 中国经济网 环球网 腾讯新闻 极客公园 东方网.
• Second place in ODIR 2019, held by Peking University.

Academic Service

I regularly serve as reviewers for following conferences and journals.

• Conference: for ICLR, ICML, NeurIPS, CVPR, ICCV, and MICCAI.
• Journal: for Nature Machine Intelligence, TPAMI, TMI, MedIA, TKDE, TMM, TIP, iScience (Cell family), and Scientific Reports.