Luyang Luo

Post-doctoral Fellow

Department of Biomedical Informatics
Harvard University

Email: luo960730 at gmail dot com

Biography

I am currently a post-doctoral fellow at the Department of Biomedical Informatics, Harvard University, working with Prof. Pranav Rajpurkar. 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 majorly focuses on developing efficient and trustworthy deep learning algorithms for medical data analysis. Currently, I am particularly interested in developing and applying foundation models for multimodal biomedical data analysis.

[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 TAI4H.
[07/2024] Survey on AI-based breast cancer imaging was sleceted as a featured article
for IEEE RBME.
[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.
[05/2024] 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] 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.
[06/2022] I passed my oral defense and became a Ph.D.
[05/2022] Two papers were early accepted
by MICCAI 2022.
[01/2022] Our ISBI2022 paper on semi-supervised cervical cancer cell detection was selected as Oral Presentation
.
[08/2021] One MICCAI2021 paper on semi-supervised COVID-19 lesion segmentation was selected as Oral Presentation
.
[06/2021] One paper on omni-supervised Chest X-ray disease detection was accepted by MICCAI.
[05/2021] One paper on semi-supervised COVID-19 lesion segmentation was early accepted
by MICCAI.
[06/2020] One paper on learning from imperfect multi-site ChestX-ray data was accepted by IEEE TMI.
[05/2020] One paper on semi-supervised learning was accepted by IEEE TMI.
[02/2020] Two papers accepted by MedIA and IEEE JBHI, respectively.
[08/2019] One paper on OCT glaucoma screening was accepted by The Lacent Digital Health and selected as the Cover Page
.
[06/2019] Two papers were early accepted
by MICCAI.

Selected Publications [Google Scholar]

* joint first authors

	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 Preprint, 2024. [project page][introduction video][demos]
	Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model Luyang Luo, Mingxiang Wu, Mei Li, Yi Xin, Qiong Wang, Varut Vardhanabhuti, Winnie CW Chu, Zhenhui Li, Juan Zhou, Pranav Rajpurkar, and Hao Chen Preprint, 2024.
	Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council Luyang Luo, Xin Huang, Minghao Wang, Zhuoyue Wan, Hao Chen Preprint, 2024.
	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), 2024. (Featured Article)
	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.
	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]
	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: 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]
	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.
	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]
	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.
	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. (Early Accept) [code][data]
	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]
	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. (Oral)* [code]
	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.
	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. (Early Accept)
	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.
	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. (Cover Page) [news]

Academic Services

Editor:
“Trustworthy Machine Learning for Healthcare”, TML4H 2023, Proceedings. [Editor]
IEEE J-BHI Special Issue on “Trustworthy Machine Learning for Health Informatics” [Guest Editor]
Program Chair&Meta Reviewer:
ICLR 2023 workshop on TML4H
ICCV CVAMD 2023
4th International Workshop on MMMI
Conference Reviews:
MICCAI 2024, 2023, 2022, 2021
CVPR 2024
AAAI 2025, 2024, 2022
ISBI 2022
Journal Reviews:
Nature Cancer
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)
IEEE Access
Frontiers in Artificial Intelligence
Frontiers in Big Data
Frontiers in Radiology

Supervised Students

Yequan Bie (PhD at HKUST)

Shu Yang (PhD at HKUST)

Zhixuan Chen (PhD at HKUST)

Cheng Jin (PhD at HKUST)

Zhiyuan Cai (PhD at HKUST)

Jiaxin Zhuang (PhD at HKUST)

Wenqiang Li (PhD at HKUST)

Xiaoyu Zheng (PhD at HKUST)

Hongyi Wang (PhD at Zhejiang University)

Zhizhong Chai (PhD at HKUST)

Minghao Wang (Msc at HKUST -> PhD at HKUST)

Dunyuan Xu (Msc at CUHK -> PhD at CUHK)

Yi Xin (MPhil at HKUST)

Yifeng Wang (Master at THU)

Teaching

2020-2021	Fall	Fundamental Computing with C++ (CSCI 1540)
2019-2020	Spring	Computer Principles and C Programming (CSCI 1510)
2019-2020	Fall	Digital Logic and Systems (ENGG 2020)
2018-2019	Fall	Digital Logic and Systems (ENGG 2020)