Charlene A. Sennett

1.4k total citations
29 papers, 1.1k citations indexed

About

Charlene A. Sennett is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine. According to data from OpenAlex, Charlene A. Sennett has authored 29 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 15 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Pathology and Forensic Medicine. Recurrent topics in Charlene A. Sennett's work include AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Breast Lesions and Carcinomas (9 papers). Charlene A. Sennett is often cited by papers focused on AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Breast Lesions and Carcinomas (9 papers). Charlene A. Sennett collaborates with scholars based in United States, Japan and South Korea. Charlene A. Sennett's co-authors include Hiroyuki Abé, Maryellen L. Giger, Gillian M. Newstead, Kirti Kulkarni, Robert A. Schmidt, Hui Li, Karen Drukker, David Schacht, Yading Yuan and Robert A. Schmidt and has published in prestigious journals such as Radiology, IEEE Transactions on Medical Imaging and American Journal of Roentgenology.

In The Last Decade

Charlene A. Sennett

29 papers receiving 1.0k citations

Peers

Charlene A. Sennett
Alexis V. Nees United States
Dianne Georgian-Smith United States
Christopher Comstock United States
Carla Wauters Netherlands
C Campassi United States
I. Schreer Germany
John J. Gisvold United States
Mary C. Lechner United States
Alexis V. Nees United States
Charlene A. Sennett
Citations per year, relative to Charlene A. Sennett Charlene A. Sennett (= 1×) peers Alexis V. Nees

Countries citing papers authored by Charlene A. Sennett

Since Specialization
Citations

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

Fields of papers citing papers by Charlene A. Sennett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charlene A. Sennett. 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 Charlene A. Sennett. The network helps show where Charlene A. Sennett may publish in the future.

Co-authorship network of co-authors of Charlene A. Sennett

This figure shows the co-authorship network connecting the top 25 collaborators of Charlene A. Sennett. A scholar is included among the top collaborators of Charlene A. Sennett 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 Charlene A. Sennett. Charlene A. Sennett 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.
Pineda, Federico, Milica Medved, Shiyang Wang, et al.. (2016). Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling. Academic Radiology. 23(9). 1137–1144. 67 indexed citations
3.
Drukker, Karen, Charlene A. Sennett, & Maryellen L. Giger. (2013). Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts. Medical Physics. 41(1). 12901–12901. 24 indexed citations
4.
Abé, Hiroyuki, David Schacht, Kirti Kulkarni, et al.. (2013). Accuracy of Axillary Lymph Node Staging in Breast Cancer Patients. Academic Radiology. 20(11). 1399–1404. 39 indexed citations
5.
Giger, Maryellen L., et al.. (2013). Automatic 3D lesion segmentation on breast ultrasound images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8670. 867025–867025. 4 indexed citations
6.
Yuan, Yading, Maryellen L. Giger, Hui Li, Neha Bhooshan, & Charlene A. Sennett. (2012). Correlative Analysis of FFDM and DCE-MRI for Improved Breast CADx. Journal of Medical and Biological Engineering. 32(1). 42–50. 8 indexed citations
7.
Li, Hui, Maryellen L. Giger, Lan Li, et al.. (2012). Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets. Journal of Digital Imaging. 25(5). 591–598. 35 indexed citations
8.
Yuan, Yading, Maryellen L. Giger, Hui Li, Neha Bhooshan, & Charlene A. Sennett. (2010). Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI. Academic Radiology. 17(9). 1158–1167. 40 indexed citations
9.
Abé, Hiroyuki, Robert A. Schmidt, Rajshri N. Shah, et al.. (2010). MR-Directed (“Second-Look”) Ultrasound Examination for Breast Lesions Detected Initially on MRI: MR and Sonographic Findings. American Journal of Roentgenology. 194(2). 370–377. 146 indexed citations
10.
Drukker, Karen, Charlene A. Sennett, & Maryellen L. Giger. (2009). Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound. IEEE Transactions on Medical Imaging. 28(1). 122–128. 31 indexed citations
11.
Drukker, Karen, Maryellen L. Giger, Ruey‐Feng Chang, et al.. (2009). Breast US Computer-aided Diagnosis System: Robustness across Urban Populations in South Korea and the United States. Radiology. 253(3). 661–671. 23 indexed citations
12.
Drukker, Karen, et al.. (2008). Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population. Radiology. 248(2). 392–397. 43 indexed citations
13.
Abé, Hiroyuki, Robert A. Schmidt, Kirti Kulkarni, et al.. (2008). Axillary Lymph Nodes Suspicious for Breast Cancer Metastasis: Sampling with US-guided 14-Gauge Core-Needle Biopsy—Clinical Experience in 100 Patients. Radiology. 250(1). 41–49. 149 indexed citations
14.
Li, Hui, Maryellen L. Giger, Yading Yuan, et al.. (2008). Evaluation of Computer-aided Diagnosis on a Large Clinical Full-field Digital Mammographic Dataset. Academic Radiology. 15(11). 1437–1445. 25 indexed citations
15.
Drukker, Karen, et al.. (2008). Performance of Breast Ultrasound Computer-aided Diagnosis. Academic Radiology. 15(10). 1234–1245. 27 indexed citations
16.
Abé, Hiroyuki, Robert A. Schmidt, Charlene A. Sennett, Akiko Shimauchi, & Gillian M. Newstead. (2007). US-guided Core Needle Biopsy of Axillary Lymph Nodes in Patients with Breast Cancer: Why and How to Do It. Radiographics. 27(suppl_1). S91–S99. 79 indexed citations
17.
Drukker, Karen, Charlene A. Sennett, & Maryellen L. Giger. (2007). The effect of image quality on the appearance of lesions on breast ultrasound: implications for CADx. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6514. 65141E–65141E. 3 indexed citations
18.
Afolabi, Adedeji, et al.. (2007). BI-RADS lexicon: An urgent call for standardization of breast ultrasound in Nigeria. 3(1). 4 indexed citations
19.
Nishikawa, Robert M., Yongyi Yang, Dezheng Huo, et al.. (2004). Observers' ability to judge the similarity of clustered calcifications on mammograms. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5372. 192–192. 13 indexed citations
20.
Sennett, Charlene A., et al.. (1989). Percapsular and percutaneous embolization of renal transplant pseudoaneurysm and AV fistula: Case report. CardioVascular and Interventional Radiology. 12(5). 270–273. 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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