World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
28744
World Ranking
3938
National Ranking
526

Research.com Recognitions

  • 2021 - IEEE Fellow For contributions to machine learning for cancer detection and diagnosis

Overview

Le Lu is affiliated with Alibaba Group (China) and has a significant body of research primarily in the intersection of medicine and computer science. Their work mainly focuses on areas including radiology, nuclear medicine and imaging, computer vision and pattern recognition, artificial intelligence, biomedical engineering, and pulmonary and respiratory medicine.

Their main topics of research encompass:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Advanced Neural Network Applications
  • Medical Imaging and Analysis
  • Lung Cancer Diagnosis and Treatment
  • Medical Imaging Techniques and Applications
  • Medical Image Segmentation Techniques

Le Lu's recent published papers include:

  • TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (2021), published in arXiv (Cornell University)
  • TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers (2024), published in Medical Image Analysis
  • Large-scale pancreatic cancer detection via non-contrast CT and deep learning (2023), published in Nature Medicine
  • LViT: Language Meets Vision Transformer in Medical Image Segmentation (2023), published in IEEE Transactions on Medical Imaging
  • Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking (2021), published in Medical Image Analysis

Frequent coauthors contributing alongside Le Lu include:

  • Jing Xiao
  • Dakai Jin
  • Adam P. Harrison
  • Xianghua Ye
  • Jiawen Yao

Le Lu has published extensively in the following venues:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • Nature Communications
  • Lecture Notes in Computer Science

They have been recognized as an IEEE Fellow since 2021, awarded specifically for contributions to machine learning for cancer detection and diagnosis.

Best Publications

  • Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu

  • ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

    Xiaosong Wang;Yifan Peng;Le Lu;Zhiyong Lu

  • TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

    Jieneng Chen;Yongyi Lu;Qihang Yu;Xiangde Luo

  • DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

    Holger R. Roth;Le Lu;Amal Farag;Hoo-Chang Shin

  • Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

    Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao

  • DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

    Ling Zhang;Le Lu;Isabella Nogues;Ronald M. Summers

  • A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    Holger R. Roth;Le Lu;Ari Seff;Kevin M. Cherry

  • TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays

    Xiaosong Wang;Yifan Peng;Le Lu;Zhiyong Lu

  • DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

    Ke Yan;Xiaosong Wang;Le Lu;Ronald M. Summers

  • Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

    Hoo-Chang Shin;Kirk Roberts;Le Lu;Dina Demner-Fushman

  • Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.

    Holger R. Roth;Le Lu;Nathan Lay;Adam P. Harrison

  • Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

    Mingchen Gao;Ulas Bagci;Le Lu;Aaron Wu

  • LViT: Language Meets Vision Transformer in Medical Image Segmentation

    Unknown

  • ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases.

    Xiaosong Wang;Yifan Peng;Le Lu;Zhiyong Lu

  • Anatomy-specific classification of medical images using deep convolutional nets

    Holger R. Roth;Christopher T. Lee;Hoo-Chang Shin;Ari Seff

  • NegBio: a high-performance tool for negation and uncertainty detection in radiology reports.

    Yifan Peng;Xiaosong Wang;Le Lu;Mohammadhadi Bagheri

  • Deep convolutional networks for pancreas segmentation in CT imaging

    Holger R. Roth;Amal Farag;Le Lu;Evrim B. Turkbey

  • A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

    Amal Farag;Le Lu;Holger R. Roth;Jiamin Liu

  • Deep Learning and Convolutional Neural Networks for Medical Image Computing

    Le Lu;Yefeng Zheng;Gustavo Carneiro;Lin Yang

  • Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking.

    Mengyang Zhao;Aadarsh Jha;Quan Liu;Bryan A. Millis

  • Interleaved text/image Deep Mining on a large-scale radiology database

    Hoo-Chang Shin;Le Lu;Lauren Kim;Ari Seff

Frequent Co-Authors

Ronald M. Summers
Ronald M. Summers National Institutes of Health
Jianhua Yao
Jianhua Yao Tencent (China)
Holger R. Roth
Holger R. Roth Nvidia (United States)
Yuankai Huo
Yuankai Huo Vanderbilt University
Lin Yang
Lin Yang University of Florida
Gregory D. Hager
Gregory D. Hager Johns Hopkins University
Jinbo Bi
Jinbo Bi University of Connecticut
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Ziyue Xu
Ziyue Xu Nvidia (United States)
Zhiyong Lu
Zhiyong Lu National Institutes of Health

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

With the rising demand for tech professionals, many students are exploring flexible options for advancing their education. An excellent choice for those seeking a faster track is an accelerated cs degree, which allows you to earn your credentials quickly and enter the workforce sooner.

For those interested in expanding beyond pure computer science, related online degrees offer exciting pathways. Consider an environmental engineering degree if you want to solve real-world sustainability challenges with technology skills.

Tech-minded students may also pursue an online mechanical engineering degree to build expertise in areas like robotics, manufacturing, or automotive design.

If you are fascinated by the mathematical foundations behind computer systems, an online theoretical physics degree provides the analytical skills that are highly valued in tech careers.

These diverse online pathways help you develop specialization and flexibility, opening doors to a wide range of rewarding STEM careers.

Best Scientists Citing Le Lu

Trending Scientists

Recently Published Articles