Luyang Luo (骆路阳)

Post-doctoral Fellow

Department of Biomedical Informatics
 Harvard Medical School

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Luyang Luo

Biography

I am currently a post-doctoral fellow at the Department of Biomedical Informatics, Harvard Medical School, where I am working with Prof. Pranav Rajpurkar. I also co-lead the Harvard/Stanford Medical AI Bootcamp. Before that, I have worked as a post-doctoral fellow with Prof. Hao Chen at HKUST. I obtained my Ph.D. degree from the CSE department, CUHK, advised by Prof. HENG Pheng-Ann and Prof. WONG, Tien-Tsin. Previously, I obtained my B.Sc. degree from the CSE department, CUHK.

My research interest lies broadly in the intersection of AI and healthcare. Currently, I am particularly interested in developing and applying foundation models for multimodal biomedical data analysis to bridging AI, patients, and doctors.

Rajpurkar Lab at Harvard DBMI

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Harvard/Stanford Medical AI Bootcamp

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News

  • [01/2025] We are hosting the Deep-Brea3th 2025 Workshop with MICCAI .
  • [02/2025] Nominated a member of Sigma Xi, The Scientific Research Honor Society .
  • [01/2025] One abstract was selected as Oral Presentation at SIIM 2025 .
  • [01/2025] One paper got accepted by Medical Image Analysis.
  • [01/2025] One paper got accepted by Nature Communications!
  • [01/2025] Invited to serve as Area Chair for MICCAI 2025!
  • [01/2025] Our paper ReXplain won the Best Paper of AI for Medicine and Healthcare, AAAI Bridge Program 2025!
  • [11/2024] Our Editorial on Trustworthy Machine Learning for Health Informatics is published!
  • [10/2024] One paper on scale-aware segmentation of lesions from medical images was accepted by IEEE TNNLS.
  • [09/2024] Awarded the Second Place in the Poster Presentation category at the 2024 Harvard DBMI Science Day.
  • [07/2024] Serve as a guest editor for the CMIG SI on TAI4MI .
  • [07/2024] Survey on AI-based breast cancer imaging was selected as a featured article for IEEE RBME.
  • [05/2024] Two papers were early accepted by MICCAI 2024.
  • [01/2024] One survey on Deep Learning in Breast Cancer Imaging was accepted by IEEE RBME.
  • [01/2024] Our paper on multimodal and explainable AI for cervical cancer diagnosis was accepted by Nature Communications.
  • [01/2024] One paper on Omni-supervised Learning was accepted by TMI.
  • [12/2023] One paper on XAI for skin lesion was accepted by AAAI 2024.
  • [08/2023] Proceedings of TML4H 2023 was officially online.
  • [06/2023] Two papers were accepted by MICCAI 2023, one of which was early accepted.
  • [05/2023] One paper on MIL-based WSI classification was accepted by IEEE TMI.
  • [05/2023] Serve as the program committee of the IJCAI 2024 workshop on TAI4H .
  • [05/2023] I have been serving as a guest editor for the IEEE JBHI SI on Trustworthy Machine Learning for Health Informatics .
  • [02/2023] One paper was accepted by MedIA.
  • [01/2023] I have been serving as the co-chair of the first ICLR 2023 workshop on TML4H .

Academic Services

Editor

Area Chair & Program Chair & Meta Reviewer

  • MICCAI 2025
  • IJCAI 2024 workshop on TAI4H
  • ICLR 2023 workshop on TML4H
  • ICCV CVAMD 2023
  • 4th International Workshop on MMMI

Conference Reviews

  • MICCAI 2024, 2023, 2022, 2021
  • CVPR 2025, 2024
  • ICCV 2025
  • AAAI 2025, 2024, 2022
  • ISBI 2022

Journal Reviews

  • Nature Cancer
  • NEJM AI
  • NPJ Digital Medicine
  • IEEE Transactions on Medical Imaging (TMI)
  • Medical Image Analysis (MedIA)
  • IEEE Transactions on Image Processing (TIP)
  • IEEE Journal of Biomedical and Health Informatics (IEEE JBHI)
  • Scientific Reports
  • Medical Physics
  • Journal of Magnetic Resonance Imaging (JMRI)
  • Expert Systems With Applications (ESWA)

Mentorship

Harvard/Stanford Medical AI Bootcamp

PhD Students at HKUST

Other Universities

Masters and MPhil Students

Selected Publications [Google Scholar]

* denotes equal contribution

ReXplain
Best Paper

ReXplain: Translating Radiology into Patient-Friendly Video Reports

Luyang Luo, Jenanan Vairavamurthy, Xiaoman Zhang, Abhinav Kumar, Ramon R. Ter-Oganesyan, Stuart T. Schroff, Dan Shilo, Rydhwana Hossain, Mike Moritz, and Pranav Rajpurkar

AAAI AIMedHeath, 2025

Project Page Video Demos Slides
PASTA

A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation

Wenhui Lei*, Hanyu Chen*, Zitian Zhang*, Luyang Luo*, Qiong Xiao, Yannian Gu, Peng Gao, Yankai Jiang, Ci Wang, Guangtao Wu, Tongjia Xu, Yingjie Zhang, Xiaofan Zhang, Pranav Rajpurkar, Shaoting Zhang, Zhenming Wang

Preprint, 2025

MOME

A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRI

Luyang Luo, Mingxiang Wu, Mei Li, Yi Xin, Qiong Wang, Varut Vardhanabhuti, Winnie CW Chu, Zhenhui Li, Juan Zhou, Pranav Rajpurkar, and Hao Chen

Nature Communications, 2025

Code
BC Survey
Featured Article

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Luyang Luo, Xi Wang, Yi Lin, Xiaoqi Ma, Andong Tan, Vince Vardhanabhuti, Winnie CW Chu, Kwang-Ting Cheng, Hao Chen

IEEE Reviews in Biomedical Engineering (IEEE RBME), 2025

Multi-source

Learning Robust Medical Image Segmentation from Multi-source Annotations

Yifeng Wang, Luyang Luo*, Mingxiang Wu, Qiong Wang, Hao Chen

Medical Image Analysis, 2025

Ada-ABC

Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council

Luyang Luo, Xin Huang, Minghao Wang, Zhuoyue Wan, Hao Chen

Preprint, 2025

MTL Scale Seg

Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images

Luyang Luo, Yanwen Li*, Huangjing Ling, Pheng-Ann Heng, Hao Chen

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024

Code
ICMIL

Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher

Hongyi Wang, Luyang Luo*, Fang Wang, Ruofeng Tong, Yen-Wei Chen, Hongjie Hu, Lanfen Lin, Hao Chen

IEEE Transactions on Medical Imaging (IEEE TMI), 2024

Code
ORFNet

Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images

Zhizhong Chai, Luyang Luo*, Huangjing Ling, Pheng-Ann Heng, Hao Chen

IEEE Transactions on Medical Imaging (IEEE TMI), 2024

Code
MICA

MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment

Yequan Bie, Luyang Luo, Hao Chen

AAAI Conference on Artificial Intelligence (AAAI), 2024

Code
OvcaFinder

Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis

Huiling Xiang*, Yongjie Xiao*, Fang Li, Chunyan Li, Lixian Liu, Tingting Deng, Cuiju Yan, Fengtao Zhou, Xi Wang, Jinjing Ou, Qingguang Lin, Ruixia Hong, Lishu Huang, Luyang Luo, Huangjing Lin, Xi Lin, Hao Chen

Nature Communications, 2024

FedLSM

Scale Federated Learning for Label Set Mismatch in Medical Image Classification

Zhipeng Deng, Luyang Luo, Hao Chen

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023

Code
RadAI

Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study

Luyang Luo, Hao Chen, Yongjie Xiao, Yanning Zhou, Xi Wang, Varut Vardhanabhuti, Mingxiang Wu, Chu Han, Zaiyi Liu, Xin Hao Benjamin Fang, Efstratios Tsougenis, Huangjing Lin, Pheng-Ann Heng

Radiology: Artificial Intelligence, 2022

PBBL
Early Accept

Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification

Luyang Luo, Dunyuan Xu, Hao Chen, Tien-Tsin Wong, Pheng-Ann Heng

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022

Code Data
OXnet

OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays

Luyang Luo, Hao Chen*, Yanning Zhou, Huangjing Lin, Pheng-Ann Heng

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021

Code
COVID-19
Oral Presentation

Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

Yanwen Li, Luyang Luo*, Huangjing Lin, Hao Chen, Pheng-Ann Heng

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021

Code
TMI

Deep Mining External Imperfect Data for Chest X-ray Disease Screening

Luyang Luo, Lequan Yu*, Hao Chen, Quande Liu, Xi Wang, Jiaqi Xu, Pheng-Ann Heng

IEEE Transactions on Medical Imaging (IEEE TMI), 2020

Angular
Early Accept

Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis

Luyang Luo, Hao Chen, Xi Wang, Qi Dou, Huangjing Lin, Juan Zhou, Gongjie Li, Pheng-Ann Heng

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019

JMRI

Weakly Supervised 3D Deep Learning for Breast Cancer Classification and Localization of the Lesions in MR Images

Juan Zhou, Luyang Luo*, Qi Dou, Hao Chen, Cheng Chen, Gong‐Jie Li, Ze‐Fei Jiang, Pheng‐Ann Heng

Journal of Magnetic Resonance Imaging (JMRI), 2019

Lancet
Cover Page

Detection of Glaucomatous Optic Neuropathy with Spectral-domain Optical Coherence Tomography: A Retrospective Training and Validation Deep-learning Analysis

An Ran Ran, Carol Y Cheung, Xi Wang, Hao Chen, Luyang Luo, Poemen P Chan, Mandy OM Wong, Robert T Chang, Suria S Mannil, Alvin L Young, Hon-wah Yung, Chi Pui Pang, Pheng-Ann Heng, Clement C Tham

The Lancet Digital Health, 2019

News

Teaching Experience

2020-2021

Fundamental Computing with C++ (CSCI 1540)

Introduction to programming concepts and practices using C++. Covers fundamental programming constructs, algorithms, and problem-solving techniques.

2019-2020

Computer Principles and C Programming (CSCI 1510)

Basic computer concepts and C programming fundamentals. Focus on programming principles and practical implementation.

2019-2020

Digital Logic and Systems (ENGG 2020)

Study of digital systems, boolean algebra, logic gates, and digital circuit design principles. Practical applications in modern digital systems.

2018-2019

Digital Logic and Systems (ENGG 2020)

Study of digital systems, boolean algebra, logic gates, and digital circuit design principles. Practical applications in modern digital systems.