Harrison X. Bai

2.1k total citations
90 papers, 1.2k citations indexed

About

Harrison X. Bai is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Harrison X. Bai has authored 90 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 29 papers in Pulmonary and Respiratory Medicine and 18 papers in Epidemiology. Recurrent topics in Harrison X. Bai's work include Radiomics and Machine Learning in Medical Imaging (23 papers), Lung Cancer Diagnosis and Treatment (11 papers) and Lymphoma Diagnosis and Treatment (8 papers). Harrison X. Bai is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (23 papers), Lung Cancer Diagnosis and Treatment (11 papers) and Lymphoma Diagnosis and Treatment (8 papers). Harrison X. Bai collaborates with scholars based in United States, China and Belarus. Harrison X. Bai's co-authors include Paul J. Zhang, Zishu Zhang, Li Yang, Ashley M. Lee, Chang Su, Giorgos C. Karakousis, Zhicheng Jiao, Bo Xiao, Raymond Y. Huang and Ihab R. Kamel and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Scientific Reports.

In The Last Decade

Harrison X. Bai

81 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Harrison X. Bai United States 21 394 365 192 154 135 90 1.2k
Mohammadhadi Bagheri United States 19 535 1.4× 313 0.9× 404 2.1× 85 0.6× 57 0.4× 66 1.5k
Zhan Feng China 22 388 1.0× 284 0.8× 530 2.8× 185 1.2× 100 0.7× 60 1.3k
Georgios Z. Papadakis United States 20 582 1.5× 347 1.0× 312 1.6× 282 1.8× 179 1.3× 83 1.6k
Mohamed Houseni United States 27 720 1.8× 468 1.3× 207 1.1× 163 1.1× 69 0.5× 70 1.8k
Te‐Chun Hsieh Taiwan 20 628 1.6× 331 0.9× 253 1.3× 98 0.6× 54 0.4× 110 1.1k
Sebastien Mulé France 20 648 1.6× 240 0.7× 233 1.2× 234 1.5× 50 0.4× 80 1.4k
Takayuki Yamada Japan 21 421 1.1× 538 1.5× 212 1.1× 87 0.6× 48 0.4× 118 1.4k
Takeyuki Watadani Japan 18 234 0.6× 632 1.7× 280 1.5× 333 2.2× 108 0.8× 49 1.6k

Countries citing papers authored by Harrison X. Bai

Since Specialization
Citations

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

Fields of papers citing papers by Harrison X. Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Harrison X. Bai. 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 Harrison X. Bai. The network helps show where Harrison X. Bai may publish in the future.

Co-authorship network of co-authors of Harrison X. Bai

This figure shows the co-authorship network connecting the top 25 collaborators of Harrison X. Bai. A scholar is included among the top collaborators of Harrison X. Bai 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 Harrison X. Bai. Harrison X. Bai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
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Wu, Jing, Alireza Mohseni, Mihir Khunte, et al.. (2025). Federated Learning for Renal Tumor Segmentation and Classification on Multi‐Center MRI Dataset. Journal of Magnetic Resonance Imaging. 62(3). 814–824.
3.
Wang, Yuli, Sun Ho Ahn, Christopher J. Mullin, et al.. (2025). Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for pulmonary embolism diagnosis and report generation from CTPA. Medical Image Analysis. 107(Pt A). 103786–103786.
4.
Zhu, Chengzhang, et al.. (2025). A Transformer utilizing bidirectional cross-attention for multi-modal classification of Age-Related Macular Degeneration. Biomedical Signal Processing and Control. 109. 107887–107887. 3 indexed citations
5.
Wu, Jing, et al.. (2025). Vision-language foundation model for 3D medical imaging. 1(1). 2 indexed citations
6.
Wang, Ningning, et al.. (2024). ECG-grained Cardiac Monitoring Using RFID. 1–9.
7.
Leary, Owen P., Yuwei Dai, Kevin Ma, et al.. (2024). MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm. American Journal of Neuroradiology. 45(10). 1536–1544. 4 indexed citations
8.
Zhuo, Zhizheng, Sven Haller, Harrison X. Bai, et al.. (2024). A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study. BMC Medicine. 22(1). 375–375. 1 indexed citations
9.
Maxwell, Aaron, et al.. (2023). Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology. 309(2). e222891–e222891. 24 indexed citations
10.
Meng, Shujuan, Thi My Linh Tran, Mingzhe Hu, et al.. (2022). End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study. Journal of Stroke and Cerebrovascular Diseases. 31(11). 106753–106753. 8 indexed citations
11.
Yi, Xiaoping, Haipeng Liu, Liping Zhu, et al.. (2021). Myosteatosis predicting risk of transition to severe COVID-19 infection. Clinical Nutrition. 41(12). 3007–3015. 29 indexed citations
12.
Kamel, Ihab R., et al.. (2021). Machine intelligence in non-invasive endocrine cancer diagnostics. Nature Reviews Endocrinology. 18(2). 81–95. 39 indexed citations
13.
Yi, Thomas, Ian Pan, Scott Collins, et al.. (2021). DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications. Journal of Digital Imaging. 34(6). 1405–1413. 6 indexed citations
14.
Kim, Minjae, Chong Hyun Suh, Sang Min Lee, et al.. (2020). Diagnostic Yield of Staging Brain MRI in Patients with Newly Diagnosed Non–Small Cell Lung Cancer. Radiology. 297(2). 419–427. 20 indexed citations
15.
Su, Chang, Kevin A. Nguyen, Harrison X. Bai, et al.. (2018). Ethnic disparity in primary cutaneous CD30+ T‐cell lymphoproliferative disorders: an analysis of 1496 cases from the US National Cancer Database. British Journal of Haematology. 181(6). 752–759. 4 indexed citations
16.
17.
Tang, Rui, Chang Su, Harrison X. Bai, et al.. (2018). Association of insurance status with survival in patients with cutaneous T-cell lymphoma. Leukemia & lymphoma. 60(5). 1253–1260. 4 indexed citations
18.
Su, Chang, Ena Agbodza, Harrison X. Bai, et al.. (2018). Publication trend, resource utilization, and impact of the US National Cancer Database. Medicine. 97(9). e9823–e9823. 39 indexed citations
19.
Neale, Natalie, James Sun, Mo Yang, et al.. (2018). Prognostic Factors in Clival Chordomas: An Integrated Analysis of 347 Patients. World Neurosurgery. 118. e375–e387. 18 indexed citations
20.

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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