R. Joe Stanley

1.9k total citations
68 papers, 1.4k citations indexed

About

R. Joe Stanley is a scholar working on Oncology, Artificial Intelligence and Epidemiology. According to data from OpenAlex, R. Joe Stanley has authored 68 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Oncology, 19 papers in Artificial Intelligence and 13 papers in Epidemiology. Recurrent topics in R. Joe Stanley's work include Cutaneous Melanoma Detection and Management (37 papers), AI in cancer detection (17 papers) and Nonmelanoma Skin Cancer Studies (12 papers). R. Joe Stanley is often cited by papers focused on Cutaneous Melanoma Detection and Management (37 papers), AI in cancer detection (17 papers) and Nonmelanoma Skin Cancer Studies (12 papers). R. Joe Stanley collaborates with scholars based in United States, Algeria and Italy. R. Joe Stanley's co-authors include William V. Stoecker, Randy H. Moss, Jason Hagerty, Harold Rabinovitz, Paul Gader, Margaret Oliviero, Rhett J Drugge, Reda Kasmi, Kapil Gupta and Haidar Almubarak and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Information Fusion and Cancers.

In The Last Decade

R. Joe Stanley

66 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R. Joe Stanley United States 21 935 658 277 254 182 68 1.4k
Catarina Barata Portugal 18 1.0k 1.1× 900 1.4× 131 0.5× 281 1.1× 249 1.4× 40 1.4k
Rahil Garnavi Australia 20 564 0.6× 486 0.7× 283 1.0× 145 0.6× 454 2.5× 52 1.4k
Lei Bi Australia 20 689 0.7× 707 1.1× 222 0.8× 267 1.1× 402 2.2× 70 1.5k
R. Bono Italy 19 539 0.6× 154 0.2× 256 0.9× 253 1.0× 259 1.4× 77 1.6k
Mohammed A. Al‐masni South Korea 17 556 0.6× 1.2k 1.8× 196 0.7× 298 1.2× 443 2.4× 51 2.0k
Randy H. Moss United States 29 2.3k 2.4× 1.6k 2.4× 721 2.6× 491 1.9× 505 2.8× 77 3.0k
Fengying Xie China 14 509 0.5× 355 0.5× 112 0.4× 157 0.6× 269 1.5× 23 839
Carmen Serrano Spain 18 257 0.3× 253 0.4× 147 0.5× 183 0.7× 194 1.1× 62 1.2k
Evgin Göçeri Türkiye 29 282 0.3× 663 1.0× 250 0.9× 158 0.6× 713 3.9× 52 1.8k
Zhen Ma China 15 340 0.4× 387 0.6× 181 0.7× 104 0.4× 467 2.6× 62 1.2k

Countries citing papers authored by R. Joe Stanley

Since Specialization
Citations

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

Fields of papers citing papers by R. Joe Stanley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. Joe Stanley

This figure shows the co-authorship network connecting the top 25 collaborators of R. Joe Stanley. A scholar is included among the top collaborators of R. Joe Stanley 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 R. Joe Stanley. R. Joe Stanley 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.
Maurya, Akanksha, R. Joe Stanley, Daniyal Saeed, et al.. (2024). Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features. Journal of Imaging Informatics in Medicine. 37(3). 1137–1150. 4 indexed citations
2.
Kasmi, Reda, et al.. (2023). SharpRazor: Automatic removal of hair and ruler marks from dermoscopy images. Skin Research and Technology. 29(4). e13203–e13203. 8 indexed citations
3.
Maurya, Akanksha, et al.. (2022). A deep learning approach to detect blood vessels in basal cell carcinoma. Skin Research and Technology. 28(4). 571–576. 12 indexed citations
4.
Kasmi, Reda, et al.. (2022). ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images. Journal of Digital Imaging. 36(2). 526–535. 18 indexed citations
5.
Smith, Colin, et al.. (2022). Deep learning-based dot and globule segmentation with pixel and blob-based metrics for evaluation. Intelligent Systems with Applications. 16. 200126–200126. 3 indexed citations
6.
Moss, Randy H., et al.. (2017). Adaptable Ring for Vision-Based Measurements and Shape Analysis. IEEE Transactions on Instrumentation and Measurement. 66(4). 746–756. 23 indexed citations
7.
Hagerty, Jason, R. Joe Stanley, & William V. Stoecker. (2017). Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?. 306–311. 4 indexed citations
8.
Kaur, Ravneet, Nabin K. Mishra, Jason Hagerty, et al.. (2016). Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images. Skin Research and Technology. 23(3). 416–428. 14 indexed citations
9.
Cheng, Beibei, R. Joe Stanley, William V. Stoecker, et al.. (2012). Automatic dirt trail analysis in dermoscopy images. Skin Research and Technology. 19(1). e20–6. 8 indexed citations
10.
Sforza, Gianluca, et al.. (2012). Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma In Situ Images. IEEE Transactions on Instrumentation and Measurement. 61(7). 1839–1847. 41 indexed citations
11.
Cheng, Beibei, et al.. (2011). Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images. Skin Research and Technology. 17(3). 278–287. 19 indexed citations
12.
Cheng, Bao‐Hui, R. Joe Stanley, William V. Stoecker, & Kerry Hinton. (2011). Automatic telangiectasia analysis in dermoscopy images using adaptive critic design. Skin Research and Technology. 18(4). 389–396. 19 indexed citations
13.
Wang, Hanzheng, Randy H. Moss, Xiaohe Chen, et al.. (2010). Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Computerized Medical Imaging and Graphics. 35(2). 116–120. 46 indexed citations
14.
Wang, Hanzheng, Xiaohe Chen, Randy H. Moss, et al.. (2010). Watershed segmentation of dermoscopy images using a watershed technique. Skin Research and Technology. 16(3). 378–84. 40 indexed citations
15.
Stoecker, William V., Kapil Gupta, Raeed H. Chowdhury, et al.. (2009). Detection of basal cell carcinoma using color and histogram measures of semitranslucent areas. Skin Research and Technology. 15(3). 283–287. 20 indexed citations
16.
Gupta, Kapil, R. Joe Stanley, William V. Stoecker, et al.. (2008). Fuzzy logic techniques for blotch feature evaluation in dermoscopy images. Computerized Medical Imaging and Graphics. 33(1). 50–57. 22 indexed citations
17.
Stanley, R. Joe, et al.. (2006). Traffic Monitoring using a Three-Dimensional Object Tracking Approach. International journal of engineering education. 22(4). 886–895. 2 indexed citations
18.
Stanley, R. Joe, Steve E. Watkins, & Randy H. Moss. (2005). Integration of Real-World Problems into an Image Processing Curriculum. International journal of engineering education. 21. 318–326. 2 indexed citations
19.
Stanley, R. Joe, et al.. (2004). Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5415. 874–874. 1 indexed citations
20.
Stanley, R. Joe & L. Rodney Long. (2001). A radius of curvature-based approach to cervical spine vertebra image analysis.. PubMed. 37. 385–90. 13 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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