Berkman Sahiner

11.8k total citations · 1 hit paper
294 papers, 8.0k citations indexed

About

Berkman Sahiner is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Berkman Sahiner has authored 294 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 190 papers in Radiology, Nuclear Medicine and Imaging, 186 papers in Artificial Intelligence and 96 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Berkman Sahiner's work include AI in cancer detection (157 papers), Radiomics and Machine Learning in Medical Imaging (139 papers) and Digital Radiography and Breast Imaging (56 papers). Berkman Sahiner is often cited by papers focused on AI in cancer detection (157 papers), Radiomics and Machine Learning in Medical Imaging (139 papers) and Digital Radiography and Breast Imaging (56 papers). Berkman Sahiner collaborates with scholars based in United States, Thailand and Malaysia. Berkman Sahiner's co-authors include Heang‐Ping Chan, Nicholas Petrick, Lubomir M. Hadjiiski, Mark A. Helvie, Mitchell M. Goodsitt, Chuan Zhou, Dorit D. Adler, Jun Wei, Aria Pezeshk and Philip N. Cascade and has published in prestigious journals such as PLoS ONE, JNCI Journal of the National Cancer Institute and Radiology.

In The Last Decade

Berkman Sahiner

284 papers receiving 7.7k citations

Hit Papers

align trajectories sort by overperformance

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.

Deep learning in medical imaging and radiation therapy

2018 527 citations Berkman Sahiner, Lubomir M. Hadjiiski et al. Medical Physics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Berkman Sahiner United States	50	5.0k	4.8k	2.6k	2.1k	969	294	8.0k
Nicholas Petrick United States	43	3.7k 0.7×	3.3k 0.7×	1.7k 0.7×	1.5k 0.7×	981 1.0×	225	6.3k
Bin Zheng United States	45	3.9k 0.8×	3.6k 0.8×	2.1k 0.8×	1.5k 0.7×	818 0.8×	392	7.8k
Lubomir M. Hadjiiski United States	49	5.4k 1.1×	4.2k 0.9×	3.1k 1.2×	1.5k 0.7×	985 1.0×	290	8.6k
Nico Karssemeijer Netherlands	53	4.9k 1.0×	5.6k 1.2×	2.7k 1.0×	1.9k 0.9×	1.7k 1.7×	200	8.6k
Robert M. Nishikawa United States	40	3.1k 0.6×	3.7k 0.8×	1.8k 0.7×	1.7k 0.8×	724 0.7×	230	5.9k
Francesco Ciompi Netherlands	28	6.6k 1.3×	5.7k 1.2×	2.1k 0.8×	3.4k 1.6×	1.2k 1.2×	103	12.6k
Heang‐Ping Chan United States	61	8.0k 1.6×	7.0k 1.5×	4.6k 1.8×	2.8k 1.3×	1.5k 1.6×	410	12.8k
Arnaud A. A. Setio Netherlands	15	5.4k 1.1×	4.4k 0.9×	1.7k 0.7×	2.9k 1.4×	611 0.6×	20	10.3k
Clara I. Sá‎nchez Netherlands	36	8.3k 1.7×	5.6k 1.2×	1.8k 0.7×	3.9k 1.9×	766 0.8×	108	14.3k
Mohsen Ghafoorian Netherlands	14	4.4k 0.9×	3.9k 0.8×	1.0k 0.4×	2.6k 1.2×	563 0.6×	27	9.3k

Countries citing papers authored by Berkman Sahiner

Since Specialization

Citations

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

Fields of papers citing papers by Berkman Sahiner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Berkman Sahiner

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

All Works

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20 of 20 papers shown

Drukker, Karen, Berkman Sahiner, Grace Hyun J. Kim, et al.. (2024). MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis. Journal of Medical Imaging. 11(2). 24504–24504. 1 indexed citations

Subbaswamy, Adarsh, et al.. (2024). A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform. npj Digital Medicine. 7(1). 334–334. 6 indexed citations

Monod, Mélodie, Peter Krusche, Qian Cao, et al.. (2024). TorchSurv: A Lightweight Package for Deep Survival Analysis. The Journal of Open Source Software. 9(104). 7341–7341. 3 indexed citations

Whitney, Heather M., Karen Drukker, Berkman Sahiner, et al.. (2023). Sequestration methodology in practice through evaluation of joint demographic distributions of 54,185 patients in the Medical Imaging and Data Resource Center (MIDRC) data commons. 7–7. 1 indexed citations

Drukker, Karen, Berkman Sahiner, Grace H. Kim, et al.. (2023). Assistance tools for the evaluation of machine learning algorithm performance: the decision tree based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort. 34. 12–12. 2 indexed citations

Whitney, Heather M., Kyle J. Myers, Karen Drukker, et al.. (2023). Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons. Journal of Medical Imaging. 10(6). 61105–61105. 4 indexed citations

Sahiner, Berkman, Weijie Chen, Ravi K. Samala, & Nicholas Petrick. (2023). Data drift in medical machine learning: implications and potential remedies. British Journal of Radiology. 96(1150). 20220878–20220878. 77 indexed citations

Gavrielides, Marios A., Qin Li, Rongping Zeng, et al.. (2013). Minimum Detectable Change in Lung Nodule Volume in a Phantom CT Study. Academic Radiology. 20(11). 1364–1370. 16 indexed citations

Wu, Yi‐Ta, Chuan Zhou, Heang‐Ping Chan, et al.. (2009). Dynamic multiple thresholding breast boundary detection algorithm for mammograms. Medical Physics. 37(1). 391–401. 12 indexed citations

10.

Ge, Jun, Heang‐Ping Chan, Berkman Sahiner, et al.. (2008). Automated detection of breast vascular calcification on full-field digital mammograms. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6915. 691517–691517. 12 indexed citations

11.

Shi, Jiazheng, Berkman Sahiner, Heang‐Ping Chan, et al.. (2008). Characterization of Mammographic Masses Based on Level Set Segmentation with New Image Features and Patient Information. PubMed Central. 1 indexed citations

12.

Shi, Jiazheng, Berkman Sahiner, Heang‐Ping Chan, et al.. (2007). Characterization of mammographic masses based on level set segmentation with new image features and patient information. Medical Physics. 35(1). 280–290. 78 indexed citations

13.

Zheng, Bin, et al.. (2007). Evaluating computer‐aided detection algorithms. Medical Physics. 34(6Part1). 2024–2038. 30 indexed citations

14.

Hadjiiski, Lubomir M., Berkman Sahiner, Sachin K. Gujar, et al.. (2007). Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation. Medical Physics. 34(11). 4399–4408. 31 indexed citations

15.

Zhou, Chuan, Heang‐Ping Chan, Berkman Sahiner, et al.. (2007). Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications. PubMed Central. 2 indexed citations

16.

Zhang, Yiheng, Heang‐Ping Chan, Berkman Sahiner, et al.. (2006). A comparative study of limited‐angle cone‐beam reconstruction methods for breast tomosynthesis. Medical Physics. 33(10). 3781–3795. 211 indexed citations

17.

Chan, Heang‐Ping, Mitchell M. Goodsitt, Mark A. Helvie, et al.. (2005). ROC study of the effect of stereoscopic imaging on assessment of breast lesions. Medical Physics. 32(4). 1001–1009. 16 indexed citations

18.

Sahiner, Berkman, Heang‐Ping Chan, Nicholas Petrick, Robert F. Wagner, & Lubomir M. Hadjiiski. (2000). Feature selection and classifier performance in computer‐aided diagnosis: The effect of finite sample size. Medical Physics. 27(7). 1509–1522. 105 indexed citations

19.

Chan, Heang‐Ping, Berkman Sahiner, Robert F. Wagner, & Nicholas Petrick. (1999). Classifier design for computer‐aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers. Medical Physics. 26(12). 2654–2668. 120 indexed citations

20.

Sahiner, Berkman, Heang‐Ping Chan, Nicholas Petrick, et al.. (1996). Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Transactions on Medical Imaging. 15(5). 598–610. 308 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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