James V. Little

1.6k total citations
30 papers, 1.2k citations indexed

About

James V. Little is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, James V. Little has authored 30 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Biomedical Engineering and 7 papers in Artificial Intelligence. Recurrent topics in James V. Little's work include Optical Imaging and Spectroscopy Techniques (12 papers), Infrared Thermography in Medicine (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). James V. Little is often cited by papers focused on Optical Imaging and Spectroscopy Techniques (12 papers), Infrared Thermography in Medicine (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). James V. Little collaborates with scholars based in United States, Spain and China. James V. Little's co-authors include Baowei Fei, Amy Y. Chen, Martin Halicek, Christopher Griffith, Mihir R. Patel, Guolan Lu, Larry L. Myers, Mark W. El‐Deiry, Baran D. Sumer and Xu Wang and has published in prestigious journals such as Scientific Reports, Clinical Cancer Research and CHEST Journal.

In The Last Decade

James V. Little

30 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James V. Little United States 17 613 324 239 214 176 30 1.2k
Martial Guillaud Canada 22 287 0.5× 288 0.9× 269 1.1× 208 1.0× 162 0.9× 107 1.5k
Muhammad Khalid Khan Niazi United States 19 604 1.0× 124 0.4× 745 3.1× 162 0.8× 130 0.7× 86 1.6k
Jihye Yun South Korea 15 564 0.9× 137 0.4× 190 0.8× 23 0.1× 323 1.8× 36 985
M. Carrara Italy 18 482 0.8× 267 0.8× 51 0.2× 99 0.5× 448 2.5× 110 1.2k
John W. Bishop United States 17 153 0.2× 144 0.4× 73 0.3× 124 0.6× 199 1.1× 54 965
Jyoti Kini India 16 326 0.5× 75 0.2× 495 2.1× 80 0.4× 65 0.4× 69 828
Robert M. Cothren United States 14 461 0.8× 422 1.3× 25 0.1× 155 0.7× 447 2.5× 31 1.1k
André Huisman Netherlands 15 402 0.7× 98 0.3× 860 3.6× 333 1.6× 77 0.4× 21 1.2k
Yanfeng Zhao China 18 408 0.7× 171 0.5× 88 0.4× 8 0.0× 231 1.3× 71 836

Countries citing papers authored by James V. Little

Since Specialization
Citations

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

Fields of papers citing papers by James V. Little

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James V. Little

This figure shows the co-authorship network connecting the top 25 collaborators of James V. Little. A scholar is included among the top collaborators of James V. Little 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 James V. Little. James V. Little 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.
Halicek, Martin, et al.. (2022). Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features. PubMed. 21. 98–98. 2 indexed citations
2.
Halicek, Martin, et al.. (2021). Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images. PubMed. 11597. 78–78. 10 indexed citations
3.
Halicek, Martin, James D. Dormer, James V. Little, Amy Y. Chen, & Baowei Fei. (2020). Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express. 11(3). 1383–1383. 77 indexed citations
4.
Halicek, Martin, Maysam Shahedi, James V. Little, et al.. (2019). Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks. Scientific Reports. 9(1). 14043–14043. 77 indexed citations
5.
Halicek, Martin, Maysam Shahedi, James V. Little, et al.. (2019). Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. PubMed. 10956. 18–18. 9 indexed citations
6.
Halicek, Martin, James D. Dormer, James V. Little, et al.. (2019). Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning. Cancers. 11(9). 1367–1367. 94 indexed citations
7.
Halicek, Martin, Himar Fabelo, Samuel Ortega, et al.. (2019). Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. PubMed. 10951. 17 indexed citations
8.
Lu, Guolan, Dongsheng Wang, Xulei Qin, et al.. (2019). Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia. Scientific Reports. 9(1). 17863–17863. 14 indexed citations
9.
Halicek, Martin, James V. Little, Mihir R. Patel, et al.. (2018). Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks. PubMed. 10576. 4–4. 36 indexed citations
10.
Halicek, Martin, Baowei Fei, James V. Little, et al.. (2018). Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks. PubMed. 10469. 33–33. 32 indexed citations
11.
Halicek, Martin, James V. Little, Xu Wang, et al.. (2018). Deformable registration of histological cancer margins to gross hyperspectral images using demons. PubMed. 121. 22–22. 10 indexed citations
12.
Lu, Guolan, James V. Little, Xu Wang, et al.. (2017). Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging. Clinical Cancer Research. 23(18). 5426–5436. 102 indexed citations
13.
Fei, Baowei, Guolan Lu, Martin Halicek, et al.. (2017). Label-free hyperspectral imaging and quantification methods for surgical margin assessment of tissue specimens of cancer patients. PubMed. 2017. 4041–4045. 16 indexed citations
14.
Little, James V., et al.. (2008). Syringomatous Adenoma of the Nipple-Treatment by Central Mound Resection and Oncoplastic Reconstruction. The Breast Journal. 14(1). 102–105. 16 indexed citations
15.
Logani, Sanjay, et al.. (2008). Vascular “Pseudo Invasion” in Laparoscopic Hysterectomy Specimens: A Diagnostic Pitfall. The American Journal of Surgical Pathology. 32(4). 560–565. 60 indexed citations
16.
Cohen, Cynthia, et al.. (2007). The utility of SMAD4 as a diagnostic immunohistochemical marker for pancreatic adenocarcinoma, and its expression in other solid tumors. Diagnostic Cytopathology. 35(10). 644–648. 21 indexed citations
17.
Nassar, Aziza, et al.. (2006). Utility of reflex gomori methenamine silver staining forPneumocystis jirovecii on bronchoalveolar lavage cytologic specimens: A review. Diagnostic Cytopathology. 34(11). 719–723. 11 indexed citations
18.
19.
Little, James V., Kathryn Foucar, Attila Horváth, & S. S. Crago. (1989). Flow cytometric analysis of lymphoma and lymphoma-like disorders.. PubMed. 6(1). 37–54. 17 indexed citations
20.
Listrom, Margaret B., et al.. (1989). Immunoreactivity of tumor-associated glycoprotein (TAG-72) in normal, hyperplastic, and neoplastic colon. Human Pathology. 20(10). 994–1000. 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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