Nathan D. Cahill

1.7k total citations
67 papers, 1.0k citations indexed

About

Nathan D. Cahill is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Nathan D. Cahill has authored 67 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 13 papers in Media Technology and 13 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Nathan D. Cahill's work include Medical Image Segmentation Techniques (19 papers), Remote-Sensing Image Classification (11 papers) and Medical Imaging Techniques and Applications (8 papers). Nathan D. Cahill is often cited by papers focused on Medical Image Segmentation Techniques (19 papers), Remote-Sensing Image Classification (11 papers) and Medical Imaging Techniques and Applications (8 papers). Nathan D. Cahill collaborates with scholars based in United States, United Kingdom and Canada. Nathan D. Cahill's co-authors include Stefi A. Baum, Andrew M. Michael, Darren A. Narayan, Christopher Kanan, Tyler L. Hayes, J. Alison Noble, Gajendra J. Katuwal, David J. Hawkes, David W. Messinger and Tonya White and has published in prestigious journals such as PLoS ONE, Monthly Notices of the Royal Astronomical Society and Schizophrenia Bulletin.

In The Last Decade

Nathan D. Cahill

62 papers receiving 956 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathan D. Cahill United States 18 311 285 189 170 123 67 1.0k
César F. Caiafa Argentina 19 334 1.1× 381 1.3× 222 1.2× 407 2.4× 96 0.8× 68 2.1k
Mohammad Sadegh Helfroush Iran 20 609 2.0× 161 0.6× 415 2.2× 209 1.2× 168 1.4× 110 1.2k
Mukund Balasubramanian United States 12 284 0.9× 260 0.9× 222 1.2× 210 1.2× 52 0.4× 27 951
Li Xiao China 21 270 0.9× 240 0.8× 159 0.8× 136 0.8× 30 0.2× 95 1.4k
A. Ravishankar Rao United States 15 636 2.0× 290 1.0× 160 0.8× 90 0.5× 111 0.9× 36 1.3k
M. Zervakis United States 15 191 0.6× 351 1.2× 96 0.5× 216 1.3× 88 0.7× 48 864
Abd‐Krim Seghouane Australia 22 332 1.1× 451 1.6× 241 1.3× 289 1.7× 68 0.6× 130 1.5k
Qiu‐Hua Lin China 16 176 0.6× 354 1.2× 110 0.6× 183 1.1× 50 0.4× 64 1.2k
Gaetano Scarano Italy 20 324 1.0× 217 0.8× 141 0.7× 48 0.3× 75 0.6× 149 1.4k
Ronen Talmon Israel 24 207 0.7× 390 1.4× 412 2.2× 50 0.3× 26 0.2× 106 1.6k

Countries citing papers authored by Nathan D. Cahill

Since Specialization
Citations

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

Fields of papers citing papers by Nathan D. Cahill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan D. Cahill. 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 Nathan D. Cahill. The network helps show where Nathan D. Cahill may publish in the future.

Co-authorship network of co-authors of Nathan D. Cahill

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan D. Cahill. A scholar is included among the top collaborators of Nathan D. Cahill 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 Nathan D. Cahill. Nathan D. Cahill 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.
2.
Cahill, Nathan D., et al.. (2023). Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda. Journal of Personalized Medicine. 13(9). 1393–1393. 4 indexed citations
3.
Loustó, C. O., Prashnna Gyawali, Linwei Wang, et al.. (2021). Vela pulsar: single pulses analysis with machine learning techniques. Monthly Notices of the Royal Astronomical Society. 509(4). 5790–5808. 3 indexed citations
4.
Wong, Tony E., George M. Thurston, Nathaniel S. Barlow, et al.. (2021). Evaluating the sensitivity of SARS-CoV-2 infection rates on college campuses to wastewater surveillance. Infectious Disease Modelling. 6. 1144–1158. 10 indexed citations
5.
Helguera, María, David T. Fetzer, David A. Shrier, et al.. (2021). A feature-based affine registration method for capturing background lung tissue deformation for ground glass nodule tracking. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 10(5). 521–539. 2 indexed citations
6.
Saber, Eli, et al.. (2020). Gradient-driven unsupervised video segmentation using deep learning techniques. Journal of Electronic Imaging. 29(1). 1–1. 2 indexed citations
7.
Hayes, Tyler L., Ronald Kemker, Nathan D. Cahill, & Christopher Kanan. (2018). New Metrics and Experimental Paradigms for Continual Learning. 2112–21123. 12 indexed citations
8.
Cahill, Nathan D., et al.. (2018). Generalized relationships between characteristic path length, efficiency, clustering coefficients, and density. Social Network Analysis and Mining. 8(1). 25 indexed citations
9.
Zhang, Chao, Nathan D. Cahill, Mohammad R. Arbabshirani, et al.. (2016). Sex and Age Effects of Functional Connectivity in Early Adulthood. Brain Connectivity. 6(9). 700–713. 88 indexed citations
10.
Katuwal, Gajendra J., Stefi A. Baum, Nathan D. Cahill, et al.. (2016). Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism. Frontiers in Neuroscience. 10. 439–439. 27 indexed citations
11.
Katuwal, Gajendra J., Stefi A. Baum, Nathan D. Cahill, & Andrew M. Michael. (2016). Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry. PLoS ONE. 11(4). e0153331–e0153331. 40 indexed citations
12.
Zhang, Xuewen, et al.. (2015). SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9472. 947209–947209. 42 indexed citations
13.
Miller, Robyn L., Andrew Michael, Tülay Adalı, et al.. (2015). Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis. Schizophrenia Bulletin. 42(1). sbv085–sbv085. 34 indexed citations
14.
Katuwal, Gajendra J., Nathan D. Cahill, Stefi A. Baum, & Andrew M. Michael. (2015). The predictive power of structural MRI in Autism diagnosis. PubMed. 2015. 4270–4273. 66 indexed citations
15.
Kushalnagar, Raja, et al.. (2012). Detecting hand-palm orientation and hand shapes for sign language gesture recognition using 3D images. 17. 29–32. 6 indexed citations
16.
17.
Michael, Andrew M., Nathan D. Cahill, Kent A. Kiehl, et al.. (2011). ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks. Frontiers in Systems Neuroscience. 5. 93–93. 29 indexed citations
18.
Cahill, Nathan D., Julia A. Schnabel, J. Alison Noble, & David J. Hawkes. (2009). Overlap invariance of cumulative residual entropy measures for multimodal image alignment. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7259. 72590I–72590I. 7 indexed citations
19.
Cahill, Nathan D., J. Alison Noble, & David J. Hawkes. (2009). A Demons Algorithm for Image Registration with Locally Adaptive Regularization. Lecture notes in computer science. 12(Pt 1). 574–581. 40 indexed citations
20.
Cahill, Nathan D. & Darren A. Narayan. (2004). Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants. ˜The œFibonacci quarterly. 42(3). 216–221. 46 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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