Alexis V. Nees

1.4k total citations
33 papers, 1.0k citations indexed

About

Alexis V. Nees is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine. According to data from OpenAlex, Alexis V. Nees has authored 33 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 20 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Pathology and Forensic Medicine. Recurrent topics in Alexis V. Nees's work include AI in cancer detection (22 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Breast Lesions and Carcinomas (12 papers). Alexis V. Nees is often cited by papers focused on AI in cancer detection (22 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Breast Lesions and Carcinomas (12 papers). Alexis V. Nees collaborates with scholars based in United States, Thailand and South Korea. Alexis V. Nees's co-authors include Mark A. Helvie, Chintana Paramagul, Lubomir M. Hadjiiski, Heang‐Ping Chan, Berkman Sahiner, Marilyn A. Roubidoux, Caroline E. Blane, Colleen H. Neal, Kathleen M. Diehl and Alfred E. Chang and has published in prestigious journals such as Radiology, Medical Physics and Annals of Surgical Oncology.

In The Last Decade

Alexis V. Nees

33 papers receiving 994 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexis V. Nees United States 17 465 440 401 394 228 33 1.0k
Charlene A. Sennett United States 18 432 0.9× 396 0.9× 578 1.4× 365 0.9× 166 0.7× 29 1.1k
Carla Wauters Netherlands 17 307 0.7× 245 0.6× 204 0.5× 323 0.8× 301 1.3× 41 1.0k
Christopher Comstock United States 17 455 1.0× 652 1.5× 1.1k 2.7× 362 0.9× 91 0.4× 60 1.7k
Dianne Georgian-Smith United States 20 595 1.3× 202 0.5× 512 1.3× 464 1.2× 161 0.7× 31 1.1k
C Campassi United States 7 648 1.4× 542 1.2× 581 1.4× 415 1.1× 115 0.5× 13 1.4k
John J. Gisvold United States 12 428 0.9× 154 0.3× 259 0.6× 341 0.9× 137 0.6× 21 841
I. Schreer Germany 15 505 1.1× 243 0.6× 392 1.0× 391 1.0× 72 0.3× 51 923
Jocelyn A. Rapelyea United States 16 494 1.1× 320 0.7× 676 1.7× 379 1.0× 80 0.4× 30 1.2k
Sujata V. Ghate United States 17 184 0.4× 402 0.9× 521 1.3× 136 0.3× 53 0.2× 38 858
Marc J. Homer United States 13 279 0.6× 133 0.3× 166 0.4× 295 0.7× 137 0.6× 31 624

Countries citing papers authored by Alexis V. Nees

Since Specialization
Citations

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

Fields of papers citing papers by Alexis V. Nees

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexis V. Nees. 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 Alexis V. Nees. The network helps show where Alexis V. Nees may publish in the future.

Co-authorship network of co-authors of Alexis V. Nees

This figure shows the co-authorship network connecting the top 25 collaborators of Alexis V. Nees. A scholar is included among the top collaborators of Alexis V. Nees 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 Alexis V. Nees. Alexis V. Nees 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.
Milliron, Kara J., et al.. (2018). Patient-friendly pathology reports for patients with breast atypias. The Breast Journal. 24(5). 855–857. 5 indexed citations
2.
Milliron, Kara J., et al.. (2018). 352 Patient-Friendly Pathology Reports for Patients With Breast Atypias. American Journal of Clinical Pathology. 149(suppl_1). S152–S152. 1 indexed citations
3.
4.
Padilla, Frédéric, Marilyn A. Roubidoux, Chintana Paramagul, et al.. (2013). Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis. Journal of Ultrasound in Medicine. 32(1). 93–104. 21 indexed citations
5.
Cho, Hyun‐chong, Lubomir M. Hadjiiski, Berkman Sahiner, et al.. (2012). A similarity study of content‐based image retrieval system for breast cancer using decision tree. Medical Physics. 40(1). 12901–12901. 9 indexed citations
6.
Cho, Hyun‐chong, Lubomir M. Hadjiiski, Berkman Sahiner, et al.. (2012). Interactive content-based image retrieval (CBIR) computer-aided diagnosis (CADx) system for ultrasound breast masses using relevance feedback. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8315. 831509–831509. 3 indexed citations
7.
Cho, Hyun‐chong, Lubomir M. Hadjiiski, Berkman Sahiner, et al.. (2011). Similarity evaluation in a content‐based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images. Medical Physics. 38(4). 1820–1831. 22 indexed citations
8.
Neal, Colleen H., et al.. (2010). Can Preoperative Axillary US Help Exclude N2 and N3 Metastatic Breast Cancer?. Radiology. 257(2). 335–341. 82 indexed citations
9.
Sahiner, Berkman, Heang‐Ping Chan, Lubomir M. Hadjiiski, et al.. (2009). Multi-modality CADx. Academic Radiology. 16(7). 810–818. 25 indexed citations
10.
Sahiner, Berkman, Lubomir M. Hadjiiski, Heang‐Ping Chan, et al.. (2009). Inter- and intra-observer variability in radiologists' assessment of mass similarity on mammograms. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7263. 726315–726315. 2 indexed citations
11.
Cui, Jing, Berkman Sahiner, Heang‐Ping Chan, et al.. (2009). A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7260. 72600L–72600L. 4 indexed citations
12.
Cui, Jing, Berkman Sahiner, Heang‐Ping Chan, et al.. (2009). A new automated method for the segmentation and characterization of breast masses on ultrasound images. Medical Physics. 36(5). 1553–1565. 30 indexed citations
13.
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
14.
Nees, Alexis V., Vincent M. Cimmino, Kathleen M. Diehl, et al.. (2008). Axillary Staging Prior to Neoadjuvant Chemotherapy for Breast Cancer: Predictors of Recurrence. Annals of Surgical Oncology. 15(11). 3252–3258. 35 indexed citations
15.
Nees, Alexis V.. (2008). Digital Mammography. Academic Radiology. 15(4). 401–407. 6 indexed citations
16.
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
17.
Sinha, Sumedha P., Marilyn A. Roubidoux, Mark A. Helvie, et al.. (2007). Multi-modality 3D breast imaging with X-Ray tomosynthesis and automated ultrasound. Conference proceedings. 2007. 1335–1338. 24 indexed citations
18.
Patterson, Stephanie K., et al.. (2006). Outcome of Men Presenting with Clinical Breast Problems: The Role of Mammography and Ultrasound. The Breast Journal. 12(5). 418–423. 39 indexed citations
19.
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
20.
Hadjiiski, Lubomir M., Heang‐Ping Chan, Berkman Sahiner, et al.. (2004). Improvement in Radiologists’ Characterization of Malignant and Benign Breast Masses on Serial Mammograms with Computer-aided Diagnosis: An ROC Study. Radiology. 233(1). 255–265. 67 indexed citations

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