Sameer Antani

15.4k total citations · 6 hit papers
331 papers, 9.7k citations indexed

About

Sameer Antani is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Sameer Antani has authored 331 papers receiving a total of 9.7k indexed citations (citations by other indexed papers that have themselves been cited), including 191 papers in Computer Vision and Pattern Recognition, 151 papers in Artificial Intelligence and 92 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Sameer Antani's work include Image Retrieval and Classification Techniques (128 papers), AI in cancer detection (126 papers) and Biomedical Text Mining and Ontologies (58 papers). Sameer Antani is often cited by papers focused on Image Retrieval and Classification Techniques (128 papers), AI in cancer detection (126 papers) and Biomedical Text Mining and Ontologies (58 papers). Sameer Antani collaborates with scholars based in United States, Thailand and United Kingdom. Sameer Antani's co-authors include George R. Thoma, L. Rodney Long, Sivaramakrishnan Rajaraman, Stefan Jaeger, Sema Candemir, Zhiyun Xue, Dina Demner‐Fushman, Clement J. McDonald, Yì Wáng and KC Santosh and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and JNCI Journal of the National Cancer Institute.

In The Last Decade

Sameer Antani

319 papers receiving 9.2k citations

Hit Papers

Two public chest X-ray datasets for computer-aided screen... 2013 2026 2017 2021 2014 2015 2013 2013 2018 200 400 600
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.

  1. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.
    2014 611 citations Stefan Jaeger, Sameer Antani et al. PubMed profile →
  2. Preparing a collection of radiology examinations for distribution and retrieval
    2015 576 citations Dina Demner‐Fushman, Marc Kohli et al. profile →
  3. Automatic Tuberculosis Screening Using Chest Radiographs
    2013 434 citations Stefan Jaeger, Zhiyun Xue et al. profile →
  4. Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
    2013 415 citations Stefan Jaeger, Zhiyun Xue et al. profile →
  5. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
    2018 329 citations Sivaramakrishnan Rajaraman, Sameer Antani et al. profile →
  6. Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays
    2020 244 citations Sivaramakrishnan Rajaraman, Sameer Antani et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sameer Antani United States 45 4.5k 4.3k 4.1k 1.0k 892 331 9.7k
Mohsen Ghafoorian Netherlands 14 4.4k 1.0× 2.6k 0.6× 3.9k 0.9× 1.0k 1.0× 521 0.6× 27 9.3k
Babak Ehteshami Bejnordi Netherlands 15 4.7k 1.1× 3.0k 0.7× 4.8k 1.2× 928 0.9× 399 0.4× 23 9.6k
Thijs Kooi Netherlands 5 4.6k 1.0× 2.6k 0.6× 4.4k 1.1× 992 1.0× 367 0.4× 10 9.2k
Clara I. Sá‎nchez Netherlands 36 8.3k 1.9× 3.9k 0.9× 5.6k 1.3× 1.8k 1.8× 574 0.6× 108 14.3k
Arnaud A. A. Setio Netherlands 15 5.4k 1.2× 2.9k 0.7× 4.4k 1.1× 1.7k 1.7× 384 0.4× 20 10.3k
Francesco Ciompi Netherlands 28 6.6k 1.5× 3.4k 0.8× 5.7k 1.4× 2.1k 2.1× 482 0.5× 103 12.6k
Metin N. Gürcan United States 37 2.4k 0.5× 2.5k 0.6× 3.9k 0.9× 529 0.5× 476 0.5× 254 6.9k
Hayit Greenspan Israel 40 2.3k 0.5× 4.6k 1.1× 2.5k 0.6× 427 0.4× 323 0.4× 182 8.3k
Baiying Lei China 48 2.4k 0.5× 2.6k 0.6× 3.1k 0.8× 369 0.4× 617 0.7× 296 8.1k
Daniel J. Mollura United States 27 3.0k 0.7× 1.6k 0.4× 1.9k 0.5× 1.1k 1.1× 684 0.8× 86 6.8k

Countries citing papers authored by Sameer Antani

Since Specialization
Citations

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

Fields of papers citing papers by Sameer Antani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sameer Antani

This figure shows the co-authorship network connecting the top 25 collaborators of Sameer Antani. A scholar is included among the top collaborators of Sameer Antani 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 Sameer Antani. Sameer Antani 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.
Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, et al.. (2024). Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. SHILAP Revista de lepidopterología. 3(1). e0000286–e0000286. 2 indexed citations
2.
Zamzmi, Ghada, Li‐Yueh Hsu, Sivaramakrishnan Rajaraman, et al.. (2023). Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure. The International Journal of Cardiovascular Imaging. 39(12). 2437–2450. 3 indexed citations
3.
Dey, Damini, Rima Arnaout, Sameer Antani, et al.. (2023). Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging. JACC. Cardiovascular imaging. 16(9). 1209–1223. 16 indexed citations
4.
Pal, Anabik, Zhiyun Xue, & Sameer Antani. (2023). Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets. Lecture notes in networks and systems. 519. 679–688. 2 indexed citations
5.
Egemen, Didem, Rebecca B. Perkins, Li C. Cheung, et al.. (2023). Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening. JNCI Journal of the National Cancer Institute. 116(1). 26–33. 25 indexed citations
6.
Yang, Feng, Ghada Zamzmi, Sivaramakrishnan Rajaraman, et al.. (2023). Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE Access. 11. 21300–21312. 31 indexed citations
7.
Antani, Sameer, et al.. (2023). The Need for Artificial Intelligence Curriculum in Military Medical Education. Military Medicine. 189(5-6). 954–958. 5 indexed citations
8.
Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, Zhiyun Xue, & Sameer Antani. (2023). Data Characterization for Reliable AI in Medicine. Communications in computer and information science. 1704. 3–11. 4 indexed citations
9.
Rajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue, & Sameer Antani. (2023). Can deep adult lung segmentation models generalize to the pediatric population?. Expert Systems with Applications. 229(Pt A). 120531–120531. 6 indexed citations
10.
Xue, Zhiyun, Feng Yang, Sivaramakrishnan Rajaraman, Ghada Zamzmi, & Sameer Antani. (2023). Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection. Diagnostics. 13(6). 1068–1068. 5 indexed citations
11.
Liang, Zhaohui, Zhiyun Xue, Sivaramakrishnan Rajaraman, Feng Yang, & Sameer Antani. (2023). Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. Lecture notes in computer science. 14307. 128–137. 1 indexed citations
12.
Desai, Kanan, Brian Befano, Zhiyun Xue, et al.. (2021). The development of “automated visual evaluation” for cervical cancer screening: The promise and challenges in adapting deep‐learning for clinical testing. International Journal of Cancer. 150(5). 741–752. 44 indexed citations
13.
Pal, Anabik, Zhiyun Xue, Brian Befano, et al.. (2021). Deep Metric Learning for Cervical Image Classification. IEEE Access. 9. 53266–53275. 31 indexed citations
14.
Rajaraman, Sivaramakrishnan, et al.. (2019). Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. PubMed. 2019. 3689–3692. 12 indexed citations
15.
Hu, Liming, David Bell, Sameer Antani, et al.. (2019). An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening. Obstetrical & Gynecological Survey. 74(6). 343–344. 21 indexed citations
16.
Rajaraman, Sivaramakrishnan, Sameer Antani, & Stefan Jaeger. (2017). Visualizing Deep Learning Activations for Improved Malaria Cell Classification.. Knowledge Discovery and Data Mining. 40–47. 10 indexed citations
17.
Simpson, Matthew, Daekeun You, Md Mahmudur Rahman, et al.. (2014). Literature-based biomedical image classification and retrieval. Computerized Medical Imaging and Graphics. 39. 3–13. 9 indexed citations
18.
Simpson, Matthew, et al.. (2013). ITI's Participation in the 2013 Medical Track of ImageCLEF. CLEF (Working Notes). 7 indexed citations
19.
Antani, Sameer, et al.. (2011). Review of medical image retrieval systems and future directions. 1–6. 39 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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