David Joon Ho

561 total citations
16 papers, 310 citations indexed

About

David Joon Ho is a scholar working on Artificial Intelligence, Biophysics and Computer Vision and Pattern Recognition. According to data from OpenAlex, David Joon Ho has authored 16 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Biophysics and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in David Joon Ho's work include AI in cancer detection (11 papers), Cell Image Analysis Techniques (9 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). David Joon Ho is often cited by papers focused on AI in cancer detection (11 papers), Cell Image Analysis Techniques (9 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). David Joon Ho collaborates with scholars based in United States, South Korea and Malaysia. David Joon Ho's co-authors include Paul Salama, Kenneth W. Dunn, Edward J. Delp, Thomas J. Fuchs, Dig Vijay Kumar Yarlagadda, Matthew G. Hanna, Lee K. Tan, Timothy M. D’Alfonso, Peter Ntiamoah and Anne Grabenstetter and has published in prestigious journals such as Cancer Research, Stroke and Scientific Reports.

In The Last Decade

David Joon Ho

16 papers receiving 300 citations

Peers

David Joon Ho

BIOPH

CVPR

RNMI

Leslie Solorzano Sweden

Shazia Akbar United Kingdom

Can Koyuncu United States

Nikita Moshkov Hungary

Rob van de Loo Netherlands

M. Milagro Fernández-Carrobles Spain

Tianhao Zhao China

Kai Saeger Germany

Roman Monczak Poland

Leslie Solorzano Sweden

David Joon Ho

137 ×0.7

114 ×0.9

BIOPH

58 ×0.5

CVPR

122 ×1.0

RNMI

30 ×0.6

Citations per year, relative to David Joon Ho David Joon Ho (= 1×) peers Leslie Solorzano

Countries citing papers authored by David Joon Ho

Since Specialization

Citations

This map shows the geographic impact of David Joon Ho's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Joon Ho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Joon Ho more than expected).

Fields of papers citing papers by David Joon Ho

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Joon Ho. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Joon Ho. The network helps show where David Joon Ho may publish in the future.

Co-authorship network of co-authors of David Joon Ho

This figure shows the co-authorship network connecting the top 25 collaborators of David Joon Ho. A scholar is included among the top collaborators of David Joon Ho based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Joon Ho. David Joon Ho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Khosravi, Pegah, Thomas J. Fuchs, & David Joon Ho. (2025). Artificial Intelligence–Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality. Cancer Research. 85(13). 2356–2367. 3 indexed citations

Sugimoto, Kazutaka, Wai-Yee Fung, David Joon Ho, et al.. (2025). Impaired Resting-State Functional Connectivity in Cerebral Autosomal-Dominant Arteriopathy, Subcortical Infarcts, and Leukoencephalopathy Mutant Mice. Stroke. 56(4). 987–995. 1 indexed citations

Wang, Zezhong, et al.. (2023). ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing. 7468–7479. 1 indexed citations

Ho, David Joon, Narasimhan P. Agaram, Chad Vanderbilt, et al.. (2022). Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction. American Journal Of Pathology. 193(3). 341–349. 15 indexed citations

Ho, David Joon, M. Herman Chui, Chad Vanderbilt, et al.. (2022). Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation. Journal of Pathology Informatics. 14. 100160–100160. 21 indexed citations

D’Alfonso, Timothy M., David Joon Ho, Matthew G. Hanna, et al.. (2021). Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens. Modern Pathology. 34(8). 1487–1494. 16 indexed citations

Ho, David Joon, Dig Vijay Kumar Yarlagadda, Timothy M. D’Alfonso, et al.. (2021). Deep Multi-Magnification Networks for multi-class breast cancer image segmentation. Computerized Medical Imaging and Graphics. 88. 101866–101866. 79 indexed citations

Ho, David Joon, et al.. (2020). Sphere estimation network: three-dimensional nuclei detection of fluorescence microscopy images. Journal of Medical Imaging. 7(4). 44003–44003. 3 indexed citations

Dunn, Kenneth W., et al.. (2019). DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. PMC. 2 indexed citations

10.

Dunn, Kenneth W., et al.. (2019). DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Scientific Reports. 9(1). 18295–18295. 63 indexed citations

11.

Ho, David Joon & Qian Lin. (2018). Person Segmentation Using Convolutional Neural Networks With Dilated Convolutions. Electronic Imaging. 30(10). 455–1. 1 indexed citations

12.

Ho, David Joon, et al.. (2018). Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation. arXiv (Cornell University). 2302–23028. 44 indexed citations

13.

Ho, David Joon, et al.. (2018). Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. 418–422. 13 indexed citations

14.

Ho, David Joon, Paul Salama, Kenneth W. Dunn, & Edward J. Delp. (2017). Boundary segmentation for fluorescence microscopy using steerable filters. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10133. 101330E–101330E. 2 indexed citations

15.

Ho, David Joon, et al.. (2017). Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks. IUScholarWorks (Indiana University). 834–842. 25 indexed citations

16.

Ho, David Joon, et al.. (2017). Nuclei segmentation of fluorescence microscopy images using convolutional neural networks. 282. 704–708. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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