Hong-Yu Zhou

Hong-Yu Zhou
Postdoctoral fellow
Department of Biomedical Informatics
Harvard Medical School
Harvard University
Email: whuzhouhongyu at gmail.com

I am a postdoctoral fellow at the Harvard DBMI, working with Pranav Rajpurkar.
My research lies in the intersection of artificial intelligence, medicine, and data science.
I develop
I am currently researching pathways to improve the scalability of medical AI systems:

Health insights at scale: How can one access a multitude of high-quality health insights (diagnoses, etc) at a notably reduced expense?
Holistic multimodal analytics: How can we leverage multimodal biomedical data (medical images, lab results, etc) to improve the modeling accuracy and to what extent can it do so?
Scaling system design: Can we improve the modeling accuracy by increasing the data or model scale, for example, integrating large language models? How far can we go?

I finished my PhD at The University of Hong Kong, advised by Yizhou Yu and my master degree at Nanjing University, advised by Jianxin Wu. Before that, I received my undergraduate degree from Wuhan University.

Paper (Selected / Full)

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.
A multimodal transformer for clinical diagnostics.
Nature Biomedical Engineering 2023 | Paper | Code | ESI - Top 1% highly cited papers

Advancing Radiograph Representation Learning with Masked Record Modeling.
Hong-Yu Zhou*, Chenyu Lian*, Liansheng Wang, Yizhou Yu.
Multimodal modeling for visual representation learning.
ICLR 2023 | Paper | Code

nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer.
Hong-Yu Zhou*, Jiansen Guo*, Yinghao Zhang*, Xiaoguang Han, Lequan Yu, Liansheng Wang, Yizhou Yu.
A widely adopted baseline for 3D medical image segmentation.
TIP 2023 | Paper | Code | ESI - Top 1% highly cited papers

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.
TPAMI 2023 | Paper | Code

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.
Nature Machine Intelligence 2023 | Paper | Code

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts.
Hong-Yu Zhou*, Chixiang Lu*, Sibei Yang, Xiaoguang Han, Yizhou Yu.
ICCV 2021 | Paper | Code

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.
MICCAI 2020 | Paper | Code

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).
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.
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.
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, ECCV, AAAI, MICCAI, etc.
Journal: for Nature, Nature Biomedical Engineering, Nature Machine Intelligence, TPAMI, TMI, MedIA, TKDE, TMM, TIP, Pattern Recognition, iScience (Cell family), etc.

Recent News

Paper (Selected / Full)

Selected Awards

Academic Service