World's Best Scientists 2026 revealed!
Jianhua Yao

Jianhua Yao

D-Index & Metrics

Computer Science

D-Index
53
Citations
16664
World Ranking
4713
National Ranking
632

Overview

Jianhua Yao is affiliated with Tencent in China and has a significant research presence across multiple scientific fields, particularly within life sciences and engineering. Their work primarily spans Biochemistry, Genetics and Molecular Biology, Medicine, and Engineering, reflecting a multidisciplinary approach to complex biological and medical challenges.

The scientist has made substantial contributions to various subfields of study including Molecular Biology, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, and Oncology. Their research covers a wide range of topics such as Single-cell and spatial transcriptomics, AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Cell Image Analysis Techniques, Monoclonal and Polyclonal Antibodies Research, Colorectal Cancer Screening and Detection, and Digital Imaging for Blood Diseases.

Jianhua Yao's publication record reflects frequent collaboration with several researchers. Notable frequent co-authors include:

  • Junzhou Huang
  • Fan Yang
  • Bing He
  • Chenchen Qin
  • Jiangning Song

The scientist has published regularly in a number of prominent venues. The most frequent publication venues are:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Nature Communications
  • Nature Methods

Recent papers authored or co-authored by Jianhua Yao demonstrate a focus on advanced techniques in biological data analysis and medical AI applications:

  • Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex, 2023, Cell
  • Benchmarking spatial clustering methods with spatially resolved transcriptomics data, 2024, Nature Methods
  • scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data, 2022, Nature Machine Intelligence
  • Early triage of critically ill COVID-19 patients using deep learning, 2020, Nature Communications
  • Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer, 2020, Theranostics

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

  • 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

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

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

  • Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

    Ronald M. Summers;Jianhua Yao;Perry J. Pickhardt;Marek Franaszek

  • Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models

    Xinjian Chen;J. K. Udupa;U. Bagci;Ying Zhuge

  • Early triage of critically ill COVID-19 patients using deep learning

    Wenhua Liang;Jianhua Yao;Ailan Chen;Qingquan Lv

  • Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.

    Rui Cao;Fan Yang;Si-Cong Ma;Li Liu

  • Interstitial Myocardial Fibrosis Assessed as Extracellular Volume Fraction with Low-Radiation-Dose Cardiac CT

    Marcelo Souto Nacif;Nadine Kawel;Jason J. Lee;Xinjian Chen

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

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

  • Computer-integrated revision total hip replacement surgery: concept and preliminary results.

    Russell H. Taylor;Leo Joskowicz;Bill Williamson;André Guéziec

  • Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

    Ulas Bagci;Jayaram K. Udupa;Neil Mendhiratta;Neil Mendhiratta;Brent Foster

  • Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

    Joseph E Burns;Jianhua Yao;Ronald M Summers

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

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

  • Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution

    Yu Zhao;Fan Yang;Yuqi Fang;Hailing Liu

  • Automated spinal column extraction and partitioning

    Jianhua Yao;S.D. O'Connor;R.M. Summers

  • Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models

    Jianhua Yao;M. Miller;M. Franaszek;R.M. Summers

  • A multi-center milestone study of clinical vertebral CT segmentation

    Jianhua Yao;Joseph E. Burns;Daniel Forsberg;Alexander Seitel

  • A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT.

    Joseph E. Burns;Jianhua Yao;Didier Chalhoub;Joseph J. Chen

  • Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification.

    Jianhua Yao;Andrew Dwyer;Ronald M. Summers;Daniel J. Mollura

  • Feasibility of simultaneous computed tomographic colonography and fully automated bone mineral densitometry in a single examination.

    Ronald M. Summers;Nicolai Baecher;Jianhua Yao;Jiamin Liu

  • Determination of feature boundaries in a digital representation of an anatomical structure

    Jianhua Yao;Ronald M. Summers

Frequent Co-Authors

Ronald M. Summers
Ronald M. Summers National Institutes of Health
Le Lu
Le Lu Alibaba Group (China)
Holger R. Roth
Holger R. Roth Nvidia (United States)
Ulas Bagci
Ulas Bagci Northwestern University
Junzhou Huang
Junzhou Huang The University of Texas at Arlington
Xinjian Chen
Xinjian Chen Soochow University
Nicholas Petrick
Nicholas Petrick US Food and Drug Administration
Joel Moss
Joel Moss National Institutes of Health
Jiang Li
Jiang Li Shanghai Jiao Tong University
Russell H. Taylor
Russell H. Taylor Johns Hopkins University

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