Ha Hong

5.4k citations

23 papers · 2.3k indexed · 3 hit papers · h-index 12

Impact in

Cognitive Neuroscience top 1%
- Neural dynamics and brain function
- Face Recognition and Perception
- Visual perception and processing mechanisms
- Functional Brain Connectivity Studies
Health Informatics top 1%

Papers in

Health Informatics 2
- Artificial Intelligence in Healthcare and Education 2
Critical Care and Intensive Care Medicine 3
- Ultrasound in Clinical Applications 3

Co-authors: James J. DiCarlo Daniel Yamins Charles F. Cadieu Ethan A. Solomon Najib J. Majaj Diego Ardila Nicolas Pinto Roberto M. Lang
Journals: Journal of the American College of Cardiology (4 papers)Circulation Cardiovascular Imaging (2 papers)Journal of Vision (2 papers)JAMA Cardiology (2 papers)Journal of Neuroscience (1 paper)
Partner nations: United States United Kingdom South Korea

In The Last Decade

Ha Hong

20 papers receiving 2.3k citations

Hit Papers

align trajectories log scale

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use 2021 · 233 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2021 JAMA Cardiology
2014 Proceedings of the National Academy of Sciences
2014 PLoS Computational Biology

Peers

Countries citing papers authored by Ha Hong

Since Specialization

Citations

This map shows the geographic impact of Ha Hong'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 Ha Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ha Hong more than expected).

Fields of papers citing papers by Ha Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ha Hong. 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 Ha Hong. The network helps show where Ha Hong may publish in the future.

Co-authors

The 25 scholars most cited alongside Ha Hong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ha Hong Line = papers co-authored together Ha Hong links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	The Role of Industry to Grow Clinical Artificial Intelligence Applications in Gastroenterology and Endoscopy Gastrointestinal Endoscopy Clinics of North America ·Austin L. Chiang, Ha Hong	2025	1
2	Self-supervised Transformation Learning for Equivariant Representations Elfadil Ahmed Ismail, Ha Hong, Jun-Mo Kim, 张瑞娟, Jaemyung Yu	2024	0
3	Deep Learning for Cardiovascular Imaging JAMA Cardiology ·Ramsey M. Wehbe, Aggelos K. Katsaggelos, Kristian J. Hammond, Ha Hong, Faraz S. Ahmad, David Ouyang, Sanjiv J. Shah, Patrick M. McCarthy, James D. Thomas	2023	27
4	AI-GUIDED ECHOCARDIOGRAPHY SYSTEM MATCHES THE IMAGE QUALITY ASSESSMENT ABILITY OF CARDIAC SONOGRAPHERS Journal of the American College of Cardiology ·Ha Hong, 于云福, Ali Chaudhry, W. Howell, Charles F. Cadieu, Yudha Cahya Maulana, James D. Thomas	2021	1
5	Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex eLife ·Xiaoxuan Jia, Ha Hong, James J. DiCarlo	2021	12
6	Deep Learning–Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution Circulation Cardiovascular Imaging ·Federico M. Asch, Victor Mor‐Avi, David Rubenson, Steven A. Goldstein, Muhamed Sarić, Issam Mikati, 于云福, Ali Chaudhry, Michael W. Rosen, Ha Hong, Elisabeth Bell, Daniel Park, José L. Díaz‐Gómez, Brandon Boesch, Sara Nikravan, Rachel Liu, Đỗ Duy Trinh Vũ Xuân Hạnh, James D. Thomas, Randolph P. Martin, Roberto M. Lang	2021	53
7	ACQUISITION OF DIAGNOSTIC ECHOCARDIOGRAPHIC IMAGES BY NOVICES USING A DEEP LEARNING BASED IMAGE GUIDANCE ALGORITHM Journal of the American College of Cardiology ·Akhil Narang, Richard Bae, Ha Hong, R. Theile, 유금도, Charles F. Cadieu, Ali Chaudhry, Randolph P. Martin, David Rubenson, Steven A. Goldstein, Stephen H. Little, Roberto M. Lang, Neil J. Weissman, James Thomas	2020	3
8	547 Point-of-care cardiac assessment using machine learning to guide image acquisition European Heart Journal - Cardiovascular Imaging ·Alan S. Pearlman, Akhil Narang, Ha Hong, Chi‐Wen Hsieh, Ali Chaudhry, Hasanah hasanah, Congqing He, 于云福, W. Howell, 정진용, Charles F. Cadieu, R P Martin, Hangying Xu, Neil J. Weissman	2020	1
9	ACCURACY AND REPRODUCIBILITY OF A NOVEL ARTIFICIAL INTELLIGENCE DEEP LEARNING-BASED ALGORITHM FOR AUTOMATED CALCULATION OF EJECTION FRACTION IN ECHOCARDIOGRAPHY Journal of the American College of Cardiology ·Federico M. Asch, Vivit Pelantri, Madeline Jankowski, C. H. P., M.J. Adams, 博嗣柳川, 정진용, Ha Hong, Roberto M. Lang	2019	8
10	INNOVATIVE ULTRASOUND TECHNOLOGIES ECHOGPS AND AUTOEF HELP NOVICES PERFORM EFFICIENT AND ACCURATE ECHOCARDIOGRAPHIC MONITORING IN CANCER PATIENTS Journal of the American College of Cardiology ·Darko Tanasković, Ali Chaudhry, Cindy Wang, Christian Ivo Stauß, Ha Hong, Michael W. Rosen, David Liang	2019	1
11	Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs Lirias (KU Leuven) ·Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Michela Franceschi, Kailyn Schmidt, Aran Nayebi, Daniel M. Bear, Daniel Yamins, James J. DiCarlo	2019	35
12	Explicit information for category-orthogonal object properties increases along the ventral stream Nature Neuroscience ·Ha Hong, Daniel Yamins, Najib J. Majaj, James J. DiCarlo	2016	206
13	Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance Journal of Neuroscience ·Najib J. Majaj, Ha Hong, Ethan A. Solomon, James J. DiCarlo	2015	99
14	Computational similarities between visual and auditory cortex studied with convolutional neural networks, fMRI, and electrophysiology Journal of Vision ·Alexander J.E. Kell, Daniel Yamins, Sam Norman-Haignere, 健赤坂, Ha Hong, K.J. Harvey, Josh H. McDermott	2015	4
15	The Emergence of Face-Selective Units in a Model that Has Never Seen a Face Journal of Vision ·Daniel Yamins, Michael Cohen, Ha Hong, Nancy Kanwisher, James J. DiCarlo	2015	0
16	Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition PLoS Computational Biology ·Charles F. Cadieu, Ha Hong, Daniel Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, James J. DiCarlo Hit paper breakdown →	2014	391
17	Performance-optimized hierarchical models predict neural responses in higher visual cortex Proceedings of the National Academy of Sciences ·Daniel Yamins, Ha Hong, Charles F. Cadieu, Ethan A. Solomon, 健赤坂, James J. DiCarlo Hit paper breakdown →	2014	997
18	Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition DSpace@MIT (Massachusetts Institute of Technology) ·Charles F. Cadieu, Ha Hong, Daniel Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, James J. DiCarlo	2014	1
19	Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream DSpace@MIT (Massachusetts Institute of Technology) ·Daniel Yamins, Ha Hong, Charles F. Cadieu, James J. DiCarlo	2013	60
20	Flickering Analysis of Erythrocyte Mechanical Properties: Dependence on Oxygenation Level, Cell Shape, and Hydration Level Biophysical Journal ·Fang-Jane Jennifer Chen, Ha Hong, Aidan T. Brown, Dong Chung Kim, Dae Joon Kang, Virgilio L. Lew, Pietro Cicuta	2009	74

About Ha Hong

Ha Hong is a scholar working on Health Informatics, Critical Care and Intensive Care Medicine, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine, having authored 23 papers that have together received 2.3k indexed citations. Recurring topics across this work include Neural dynamics and brain function (8 papers), Face Recognition and Perception (8 papers), Visual perception and processing mechanisms (6 papers), Cardiac Imaging and Diagnostics (6 papers), Ultrasound in Clinical Applications (3 papers), Cardiovascular Function and Risk Factors (3 papers), Visual Attention and Saliency Detection (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). The work is most often cited by research in Cognitive Neuroscience (1.5k citations), Health Informatics (91 citations), Computer Vision and Pattern Recognition (544 citations), Critical Care and Intensive Care Medicine (125 citations) and Biophysics (119 citations). Ha Hong has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include James J. DiCarlo, Daniel Yamins, Charles F. Cadieu, Ethan A. Solomon, Najib J. Majaj, Diego Ardila, Nicolas Pinto, Roberto M. Lang, Randolph P. Martin and James D. Thomas. Their work appears in journals such as Journal of the American College of Cardiology, Circulation Cardiovascular Imaging, Journal of Vision, JAMA Cardiology and Journal of Neuroscience.

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.

Explore authors with similar magnitude of impact