Anup Tuladhar

1.0k total citations
23 papers, 726 citations indexed

About

Anup Tuladhar is a scholar working on Cellular and Molecular Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Anup Tuladhar has authored 23 papers receiving a total of 726 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cellular and Molecular Neuroscience, 6 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Anup Tuladhar's work include Nerve injury and regeneration (6 papers), Neurogenesis and neuroplasticity mechanisms (5 papers) and Machine Learning in Healthcare (4 papers). Anup Tuladhar is often cited by papers focused on Nerve injury and regeneration (6 papers), Neurogenesis and neuroplasticity mechanisms (5 papers) and Machine Learning in Healthcare (4 papers). Anup Tuladhar collaborates with scholars based in Canada, Germany and United Kingdom. Anup Tuladhar's co-authors include Molly S. Shoichet, Cindi M. Morshead, Jaclyn M. Obermeyer, Samantha L. Payne, Nils D. Forkert, Christopher K. McLaughlin, Tyler N. Shendruk, Malgosia M. Pakulska, Irja Elliott Donaghue and Matthias Wilms and has published in prestigious journals such as PLoS ONE, Biomaterials and Journal of Controlled Release.

In The Last Decade

Anup Tuladhar

23 papers receiving 718 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anup Tuladhar Canada 14 187 172 158 135 80 23 726
Timothy Chang United States 13 137 0.7× 156 0.9× 44 0.3× 185 1.4× 57 0.7× 32 915
Shenglan Li China 21 805 4.3× 135 0.8× 43 0.3× 181 1.3× 88 1.1× 58 1.5k
Douglas R. Martin United States 26 939 5.0× 134 0.8× 69 0.4× 72 0.5× 53 0.7× 70 1.8k
Henry C. Tseng United States 19 276 1.5× 72 0.4× 22 0.1× 63 0.5× 73 0.9× 57 883
Chris S. Bjornsson United States 12 175 0.9× 343 2.0× 93 0.6× 619 4.6× 39 0.5× 20 1.1k
Roy M. Smeal United States 12 121 0.6× 387 2.3× 90 0.6× 117 0.9× 68 0.8× 13 740
David J. Logan United States 12 668 3.6× 119 0.7× 25 0.2× 193 1.4× 57 0.7× 16 1.5k
Haoyu Wang China 13 240 1.3× 129 0.8× 63 0.4× 125 0.9× 35 0.4× 53 677
Chiara Magliaro Italy 15 214 1.1× 97 0.6× 69 0.4× 249 1.8× 16 0.2× 36 594

Countries citing papers authored by Anup Tuladhar

Since Specialization
Citations

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

Fields of papers citing papers by Anup Tuladhar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anup Tuladhar. 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 Anup Tuladhar. The network helps show where Anup Tuladhar may publish in the future.

Co-authorship network of co-authors of Anup Tuladhar

This figure shows the co-authorship network connecting the top 25 collaborators of Anup Tuladhar. A scholar is included among the top collaborators of Anup Tuladhar 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 Anup Tuladhar. Anup Tuladhar 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.
Tuladhar, Anup, Matthias Wilms, Deepthi Rajashekar, et al.. (2023). Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. International Journal of Computer Assisted Radiology and Surgery. 18(5). 827–836. 6 indexed citations
2.
Tuladhar, Anup, et al.. (2022). Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. Neuroinformatics. 21(1). 45–55. 6 indexed citations
3.
Mouchès, Pauline, et al.. (2022). An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. Journal of the American Medical Informatics Association. 30(1). 112–119. 8 indexed citations
4.
Subbanna, Nagesh K., Matthias Wilms, Anup Tuladhar, & Nils D. Forkert. (2021). An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks. Sensors. 21(11). 3874–3874. 15 indexed citations
5.
Tuladhar, Anup, et al.. (2021). Modeling Neurodegeneration in silico With Deep Learning. Frontiers in Neuroinformatics. 15. 748370–748370. 11 indexed citations
6.
Tuladhar, Anup, et al.. (2020). Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks. IEEE Access. 8. 94871–94879. 25 indexed citations
7.
Vercio, Lucas Lo, Jordan J. Bannister, M. Ethan MacDonald, et al.. (2020). Supervised machine learning tools: a tutorial for clinicians. Journal of Neural Engineering. 17(6). 62001–62001. 103 indexed citations
8.
Tuladhar, Anup, Sascha Gill, Zahinoor Ismail, & Nils D. Forkert. (2020). Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling. Journal of Biomedical Informatics. 106. 103424–103424. 22 indexed citations
9.
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11.
Obermeyer, Jaclyn M., Anup Tuladhar, Samantha L. Payne, et al.. (2019). Local Delivery of Brain-Derived Neurotrophic Factor Enables Behavioral Recovery and Tissue Repair in Stroke-Injured Rats. Tissue Engineering Part A. 25(15-16). 1175–1187. 42 indexed citations
12.
Payne, Samantha L., Anup Tuladhar, Jaclyn M. Obermeyer, et al.. (2018). Initial cell maturity changes following transplantation in a hyaluronan-based hydrogel and impacts therapeutic success in the stroke-injured rodent brain. Biomaterials. 192. 309–322. 40 indexed citations
13.
Tuladhar, Anup, Samantha L. Payne, & Molly S. Shoichet. (2018). Harnessing the Potential of Biomaterials for Brain Repair after Stroke. Frontiers in Materials. 5. 34 indexed citations
14.
Pakulska, Malgosia M., Irja Elliott Donaghue, Jaclyn M. Obermeyer, et al.. (2016). Encapsulation-free controlled release: Electrostatic adsorption eliminates the need for protein encapsulation in PLGA nanoparticles. Science Advances. 2(5). 13–14. 132 indexed citations
15.
Tuladhar, Anup, et al.. (2016). Therapeutic hyaluronan-based hydrogel enables local drug delivery for recovery after stroke. Frontiers in Bioengineering and Biotechnology. 4. 1 indexed citations
16.
Tuladhar, Anup, Cindi M. Morshead, & Molly S. Shoichet. (2015). Circumventing the blood–brain barrier: Local delivery of cyclosporin A stimulates stem cells in stroke-injured rat brain. Journal of Controlled Release. 215. 1–11. 58 indexed citations
17.
Cooke, Michael J., et al.. (2013). A hydrogel composite system for sustained epi-cortical delivery of Cyclosporin A to the brain for treatment of stroke. Journal of Controlled Release. 166(3). 197–202. 57 indexed citations
18.
Sokolenko, Stanislav, et al.. (2012). Co-expression vs. co-infection using baculovirus expression vectors in insect cell culture: Benefits and drawbacks. Biotechnology Advances. 30(3). 766–781. 66 indexed citations
19.
Takeuchi, Tatsuto, et al.. (2011). The effect of retinal illuminance on visual motion priming. Vision Research. 51(10). 1137–1145. 14 indexed citations
20.
Takeuchi, Tatsuto, et al.. (2011). Estimation of Mental Effort in Learning Visual Search by Measuring Pupil Response. PLoS ONE. 6(7). e21973–e21973. 16 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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