Jun Kong

Jun Kong

Emory & Henry College

H-index: 33

North America-United States

About Jun Kong

Jun Kong, With an exceptional h-index of 33 and a recent h-index of 23 (since 2020), a distinguished researcher at Emory & Henry College, specializes in the field of Whole-slide Microscopy Image Processing, Bioimage Informaitcs, Machine Learning, Computer-aided Diagnosis.

His recent articles reflect a diverse array of research interests and contributions to the field:

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification

Effective and efficient active learning for deep learning-based tissue image analysis

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

Deep learning based registration of serial whole-slide histopathology images in different stains

Efficient spatial queries over complex polygons with hybrid representations

Banff Digital Pathology Working Group: Image Bank

Jun Kong Information

University

Position

___

Citations(all)

4100

Citations(since 2020)

1870

Cited By

3016

hIndex(all)

33

hIndex(since 2020)

23

i10Index(all)

82

i10Index(since 2020)

52

Email

University Profile Page

Google Scholar

Jun Kong Skills & Research Interests

Whole-slide Microscopy Image Processing

Bioimage Informaitcs

Machine Learning

Computer-aided Diagnosis

Top articles of Jun Kong

Title

Journal

Author(s)

Publication Date

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Breast Cancer Research

Timothy B Fisher

Geetanjali Saini

TS Rekha

Jayashree Krishnamurthy

Shristi Bhattarai

...

2024/1/18

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification

arXiv preprint arXiv:2403.18134

Zhan Shi

Jingwei Zhang

Jun Kong

Fusheng Wang

2024/3/26

Effective and efficient active learning for deep learning-based tissue image analysis

Bioinformatics

André LS Meirelles

Tahsin Kurc

Jun Kong

Renato Ferreira

Joel Saltz

...

2023/4/1

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Diagnostics

Shristi Bhattarai

Geetanjali Saini

Hongxiao Li

Gaurav Seth

Timothy B Fisher

...

2023/12/28

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

Transplant International

Alton B Farris

Mariam P Alexander

Ulysses GJ Balis

Laura Barisoni

Peter Boor

...

2023

Deep learning based registration of serial whole-slide histopathology images in different stains

Journal of Pathology Informatics

Mousumi Roy

Fusheng Wang

George Teodoro

Shristi Bhattarai

Mahak Bhargava

...

2023/1/1

Efficient spatial queries over complex polygons with hybrid representations

GeoInformatica

Dejun Teng

Furqan Baig

Zhaohui Peng

Jun Kong

Fusheng Wang

2023/12/27

Banff Digital Pathology Working Group: Image Bank

Artificial Intelligence Algorithm, and Challenge Trial Developments. Transpl Int 36: 11783. doi: 10.3389/ti

AB Farris

MP Alexander

UGJ Balis

L Barisoni

P Boor

...

2023/10/16

Self-supervised semantic segmentation of retinal pigment epithelium cells in flatmount fluorescent microscopy images

Bioinformatics

Hanyi Yu

Fusheng Wang

George Teodoro

Fan Chen

Xiaoyuan Guo

...

2023/4/1

Real-time spatial registration for 3D human atlas

Lu Chen

Dejun Teng

Tian Zhu

Jun Kong

Bruce W Herr

...

2022/11/1

MultiHeadGAN: A deep learning method for low contrast retinal pigment epithelium cell segmentation with fluorescent flatmount microscopy images

Computers in Biology and Medicine

Hanyi Yu

Fusheng Wang

George Teodoro

John Nickerson

Jun Kong

2022/7/1

Abstract P1-08-16: Using machine learning approaches to predict response to neoadjuvant chemotherapy in patients with triple-negative breast cancer

Cancer Research

Timothy Byron Fisher

Hongxiao Li

Rekha TS

Jayashree Krishnamurthy

Shristi Bhattarai

...

2022/2/15

Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images

Computers in Biology and Medicine

Hanyi Yu

Nima Sharifai

Kun Jiang

Fusheng Wang

George Teodoro

...

2022/11/1

Deep learning-based pathology image analysis enhances magee feature correlation with oncotype DX breast recurrence score

Frontiers in Medicine

Hongxiao Li

Jigang Wang

Zaibo Li

Melad Dababneh

Fusheng Wang

...

2022/6/14

Efficient 3D spatial queries for complex objects

ACM Transactions on Spatial Algorithms and Systems (TSAS)

Dejun Teng

Yanhui Liang

Hoang Vo

Jun Kong

Fusheng Wang

2022/2/12

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images

Bioinformatics

Hongyi Duanmu

Shristi Bhattarai

Hongxiao Li

Zhan Shi

Fusheng Wang

...

2022/10/1

Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

Journal of Parallel and Distributed Computing

Willian Barreiros Jr

Alba CMA Melo

Jun Kong

Renato Ferreira

Tahsin M Kurc

...

2022/6/1

Tensile force-induced cytoskeletal remodeling: mechanics before chemistry

Biophysical Journal

Xiaona Li

Qin Ni

Xiuxiu He

Jun Kong

Soon-Mi Lim

...

2022/2/11

Heterogeneous Data Management, Polystores, and Analytics for Healthcare: VLDB Workshops, Poly 2022 and DMAH 2022, Virtual Event, September 9, 2022, Revised Selected Papers

Vijay Gadepally

Timothy Mattson

Michael Stonebraker

Tim Kraska

Fusheng Wang

...

2021/3/3

Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer

Geetanjali Saini

Shriya Joshi

Chakravarthy Garlapati

Hongxiao Li

Jun Kong

...

2022/6/1

See List of Professors in Jun Kong University(Emory & Henry College)

Co-Authors

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