Predrag R. Bakić

2.7k total citations
157 papers, 1.7k citations indexed

About

Predrag R. Bakić is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Predrag R. Bakić has authored 157 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 110 papers in Pulmonary and Respiratory Medicine, 105 papers in Radiology, Nuclear Medicine and Imaging and 77 papers in Artificial Intelligence. Recurrent topics in Predrag R. Bakić's work include Digital Radiography and Breast Imaging (110 papers), AI in cancer detection (75 papers) and Medical Imaging Techniques and Applications (74 papers). Predrag R. Bakić is often cited by papers focused on Digital Radiography and Breast Imaging (110 papers), AI in cancer detection (75 papers) and Medical Imaging Techniques and Applications (74 papers). Predrag R. Bakić collaborates with scholars based in United States, Sweden and Brazil. Predrag R. Bakić's co-authors include Andrew D. A. Maidment, Michael Albert, D. Brzaković, David D. Pokrajac, Despina Kontos, Bruno Barufaldi, Cuiping Zhang, Andrea B. Troxel, Kyle J. Myers and Subok Park and has published in prestigious journals such as Current Biology, Radiology and Expert Systems with Applications.

In The Last Decade

Predrag R. Bakić

141 papers receiving 1.7k citations

Peers

Predrag R. Bakić

RNMI

PRM

CVPR

Nico Karssemeijer Netherlands

Aaron D. Ward Canada

Serge Muller France

Ali Kamen United States

Lena Costaridou Greece

Noboru Niki Japan

Jonas Teuwen Netherlands

Volker Dicken Germany

John Heine United States

Hidetaka Arimura Japan

Nico Karssemeijer Netherlands

Predrag R. Bakić

955 ×0.7

RNMI

645 ×0.5

PRM

645 ×0.8

255 ×0.5

470 ×2.0

CVPR

Citations per year, relative to Predrag R. Bakić Predrag R. Bakić (= 1×) peers Nico Karssemeijer

Countries citing papers authored by Predrag R. Bakić

Since Specialization

Citations

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

Fields of papers citing papers by Predrag R. Bakić

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Predrag R. Bakić. 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 Predrag R. Bakić. The network helps show where Predrag R. Bakić may publish in the future.

Co-authorship network of co-authors of Predrag R. Bakić

This figure shows the co-authorship network connecting the top 25 collaborators of Predrag R. Bakić. A scholar is included among the top collaborators of Predrag R. Bakić 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 Predrag R. Bakić. Predrag R. Bakić 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

Jögi, Annika, Kristin Johnson, Anna Åkesson, et al.. (2024). Assessing Digital Breast Tomosynthesis Impact on Early Cancer Detection: Insights from Consecutive Screening. Radiology. 312(1). e233417–e233417.

Zackrisson, Sophia, et al.. (2024). Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging. Journal of Medical Imaging. 12(S1). S13003–S13003.

Hill, David, Magnus Dustler, Sophia Zackrisson, et al.. (2024). Simulation of ultrasound optical tomography (UOT) for characterizing breast tumors. Lund University Publications (Lund University). 11923. 58–58.

Dustler, Magnus, Predrag R. Bakić, Kristin Johnson, et al.. (2023). Malmö Breast ImaginG database: objectives and development. Journal of Medical Imaging. 10(6). 61402–61402.

Vieira, Marcelo A. C., Sophia Zackrisson, Anders Tingberg, et al.. (2023). Simulation of breast lesions based upon fractal Perlin noise. Physica Medica. 114. 102681–102681. 3 indexed citations

Bernhardsson, Christian, et al.. (2023). Dose evaluation of simultaneous breast radiography and mechanical imaging. Lund University Publications (Lund University). 166–166. 1 indexed citations

Kimpe, Tom, et al.. (2020). Simulation and evaluation of clinically relevant features in a computational skin model. Ghent University Academic Bibliography (Ghent University). 1 indexed citations

Acciavatti, Raymond J., Eric A. Cohen, Omid Haji Maghsoudi, et al.. (2020). Calculation of radiomic features to validate the textural realism of physical anthropomorphic phantoms for digital mammography. PubMed. 11513. 101–101. 2 indexed citations

Acciavatti, Raymond J., Ingrid Reiser, Ioannis Sechopoulos, et al.. (2018). Analysis of volume overestimation artifacts in the breast outline segmentation in tomosynthesis. PubMed. 10573. 195–195. 14 indexed citations

10.

Vieira, Marcelo A. C., et al.. (2015). Investigating poisson noise filtering in Digital Breast Tomosynthesis. Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico). 1 indexed citations

11.

Lago, Miguel A., et al.. (2015). Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues. Expert Systems with Applications. 42(21). 7942–7950. 6 indexed citations

12.

Zeng, Rongping, Subok Park, Predrag R. Bakić, & Kyle J. Myers. (2015). Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study. Physics in Medicine and Biology. 60(3). 1259–1288. 39 indexed citations

13.

Young, Stefano, Predrag R. Bakić, Kyle J. Myers, Robert J. Jennings, & Subok Park. (2013). A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. Medical Physics. 40(5). 51914–51914. 55 indexed citations

14.

Bakić, Predrag R., et al.. (2011). Development of a physical 3D anthropomorphic breast phantom. Medical Physics. 38(2). 891–896. 84 indexed citations

15.

Cheng, Erkang, Haibin Ling, Predrag R. Bakić, Andrew D. A. Maidment, & Vasileios Megalooikonomou. (2011). AUTOMATIC DETECTION OF REGION OF INTERESTS IN MAMMOGRAPHIC IMAGES. 12(31). 1 indexed citations

16.

Richard, Frédéric, Predrag R. Bakić, & Andrew D. A. Maidment. (2006). Mammogram registration: a phantom-based evaluation of compressed Breast Thickness variation effects. IEEE Transactions on Medical Imaging. 25(2). 188–197. 19 indexed citations

17.

Maidment, Andrew D. A., Predrag R. Bakić, & Michael Albert. (2003). Effects of quantum noise and binocular summation on dose requirements in stereoradiography. Medical Physics. 30(12). 3061–3071. 15 indexed citations

18.

Bakić, Predrag R., Michael Albert, D. Brzaković, & Andrew D. A. Maidment. (2003). Mammogram synthesis using a three‐dimensional simulation. III. Modeling and evaluation of the breast ductal network. Medical Physics. 30(7). 1914–1925. 47 indexed citations

19.

Bakić, Predrag R., Michael Albert, & Andrew D. A. Maidment. (2003). Classification of Galactograms with Ramification Matrices. Academic Radiology. 10(2). 198–204. 16 indexed citations

20.

Bakić, Predrag R., Michael Albert, D. Brzaković, & Andrew D. A. Maidment. (2002). Mammogram synthesis using a 3D simulation. II. Evaluation of synthetic mammogram texture. Medical Physics. 29(9). 2140–2151. 68 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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