Daniel Tse

5.7k total citations · 1 hit paper
7 papers, 1.5k citations indexed

About

Daniel Tse is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Daniel Tse has authored 7 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Pulmonary and Respiratory Medicine, 5 papers in Radiology, Nuclear Medicine and Imaging and 1 paper in Pediatrics, Perinatology and Child Health. Recurrent topics in Daniel Tse's work include Lung Cancer Diagnosis and Treatment (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and COVID-19 diagnosis using AI (3 papers). Daniel Tse is often cited by papers focused on Lung Cancer Diagnosis and Treatment (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and COVID-19 diagnosis using AI (3 papers). Daniel Tse collaborates with scholars based in United States and Hong Kong. Daniel Tse's co-authors include Joshua Reicher, Greg S. Corrado, Mozziyar Etemadi, Wenxing Ye, Lily Peng, Atilla P. Kiraly, Safal Shetty, Sujeeth Bharadwaj, David P. Naidich and Diego Ardila and has published in prestigious journals such as Nature Medicine, Radiology and JAMA Network Open.

In The Last Decade

Daniel Tse

7 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

2019 1.2k citations Diego Ardila, Atilla P. Kiraly et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Tse United States	6	991	542	476	268	196	7	1.5k
Sujeeth Bharadwaj United States	4	830 0.8×	481 0.9×	425 0.9×	195 0.7×	169 0.9×	6	1.2k
Joshua Reicher United States	10	1.1k 1.1×	613 1.1×	488 1.0×	291 1.1×	228 1.2×	26	1.8k
Hari Trivedi United States	19	876 0.9×	534 1.0×	509 1.1×	534 2.0×	257 1.3×	68	1.9k
Paras Lakhani United States	18	1.3k 1.4×	498 0.9×	517 1.1×	343 1.3×	255 1.3×	39	2.0k
Jonas Teuwen Netherlands	15	962 1.0×	268 0.5×	671 1.4×	180 0.7×	216 1.1×	62	1.4k
Yunfei Zha China	16	1.4k 1.4×	270 0.5×	653 1.4×	286 1.1×	167 0.9×	74	1.9k
J. Titano United States	14	746 0.8×	294 0.5×	518 1.1×	394 1.5×	176 0.9×	31	1.7k
Ian Pan United States	14	1.2k 1.2×	220 0.4×	357 0.8×	335 1.3×	259 1.3×	31	1.8k
George Shih United States	19	1000 1.0×	200 0.4×	337 0.7×	347 1.3×	204 1.0×	70	1.6k
Junjie Bai China	14	1.4k 1.4×	272 0.5×	677 1.4×	327 1.2×	167 0.9×	37	2.0k

Countries citing papers authored by Daniel Tse

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Tse

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Tse

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

All Works

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

7 of 7 papers shown

1.

Chen, Christina, et al.. (2023). Development of a Machine Learning Model for Sonographic Assessment of Gestational Age. JAMA Network Open. 6(1). e2248685–e2248685. 19 indexed citations

2.

Chen, Christina, Aaron Sarna, Charles T. Lau, et al.. (2022). Simplified Transfer Learning for Chest Radiography Models Using Less Data. Radiology. 305(2). 454–465. 30 indexed citations

3.

Duggan, Gavin E., Joshua Reicher, Yun Liu, Daniel Tse, & Shravya Shetty. (2021). Improving reference standards for validation of AI-based radiography. British Journal of Radiology. 94(1123). 20210435–20210435. 13 indexed citations

4.

Ardila, Diego, Atilla P. Kiraly, Sujeeth Bharadwaj, et al.. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine. 25(6). 954–961. 1221 indexed citations breakdown →

5.

Steiner, David F., Joshua Reicher, Scott Mayer McKinney, et al.. (2019). Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation. Radiology. 294(2). 421–431. 171 indexed citations

6.

Ardila, Diego, Atilla P. Kiraly, Sujeeth Bharadwaj, et al.. (2018). Improving the specificity of lung cancer screening CT using deep learning. 1 indexed citations

7.

Tse, Daniel, et al.. (2006). Perception of Doctors and Nurses on the Care and Bereavement Support for Relatives of Terminally Ill Patients in an Acute Setting. Hong Kong journal of psychiatry. 16(1). 7. 9 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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