Daniela Ushizima

2.0k total citations
85 papers, 1.2k citations indexed

About

Daniela Ushizima is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics. According to data from OpenAlex, Daniela Ushizima has authored 85 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 17 papers in Biophysics. Recurrent topics in Daniela Ushizima's work include AI in cancer detection (21 papers), Medical Image Segmentation Techniques (18 papers) and Cell Image Analysis Techniques (17 papers). Daniela Ushizima is often cited by papers focused on AI in cancer detection (21 papers), Medical Image Segmentation Techniques (18 papers) and Cell Image Analysis Techniques (17 papers). Daniela Ushizima collaborates with scholars based in United States, Brazil and Canada. Daniela Ushizima's co-authors include Andrea Bianchi, Cláudia Martins Carneiro, F.N.S. Medeiros, Fátima N. S. de Medeiros, Mariana T. Rezende, Romuere Silva, Flávio H. D. Araújo, Dilworth Y. Parkinson, Masoud S. Nosrati and Andrew P. Bradley and has published in prestigious journals such as SHILAP Revista de lepidopterología, Advanced Functional Materials and NeuroImage.

In The Last Decade

Daniela Ushizima

80 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
Daniela Ushizima United States 22 420 392 248 184 165 85 1.2k
Teresa Mendonça Portugal 23 904 2.2× 281 0.7× 118 0.5× 291 1.6× 363 2.2× 135 2.7k
Gregor Urban United States 12 381 0.9× 398 1.0× 454 1.8× 45 0.2× 122 0.7× 24 1.6k
Zhihao Wu China 9 343 0.8× 341 0.9× 429 1.7× 28 0.2× 99 0.6× 16 1.0k
Yun Jiang China 18 341 0.8× 471 1.2× 577 2.3× 58 0.3× 185 1.1× 64 1.3k
Nilanjan Ray Canada 27 418 1.0× 1.2k 3.1× 184 0.7× 23 0.1× 141 0.9× 137 2.4k
Junji Maeda Japan 14 250 0.6× 287 0.7× 62 0.3× 70 0.4× 14 0.1× 95 1.0k
Fei Shi China 27 460 1.1× 1.0k 2.7× 1.6k 6.4× 77 0.4× 51 0.3× 174 2.8k
Paul F. Whelan Ireland 28 193 0.5× 1.5k 3.8× 413 1.7× 22 0.1× 352 2.1× 145 2.8k
Guanglei Wang China 16 165 0.4× 200 0.5× 140 0.6× 18 0.1× 146 0.9× 71 1.0k
Liangqiong Qu China 19 366 0.9× 542 1.4× 347 1.4× 13 0.1× 240 1.5× 43 1.6k

Countries citing papers authored by Daniela Ushizima

Since Specialization
Citations

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

Fields of papers citing papers by Daniela Ushizima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniela Ushizima

This figure shows the co-authorship network connecting the top 25 collaborators of Daniela Ushizima. A scholar is included among the top collaborators of Daniela Ushizima 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 Daniela Ushizima. Daniela Ushizima 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.
Pandolfi, Ronald, et al.. (2025). ASCRIBE-XR: Extended Reality for Visualization of Scientific Images. 259–268.
2.
Andeer, Peter, et al.. (2024). RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs. Scientific Reports. 14(1). 12907–12907. 3 indexed citations
3.
Huang, Ying, David H. Perlmutter, Pavel Shevchenko, et al.. (2023). Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning. npj Computational Materials. 9(1). 32 indexed citations
4.
Zenyuk, Iryna V., et al.. (2023). Lithium Metal Battery Quality Control via Transformer–CNN Segmentation. Journal of Imaging. 9(6). 111–111. 3 indexed citations
5.
Rezende, Mariana T., Andrea Bianchi, Cláudia Martins Carneiro, et al.. (2022). A Cytopathologist Eye Assistant for Cell Screening. SHILAP Revista de lepidopterología. 2(4). 659–674.
6.
Rezende, Mariana T., et al.. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific Data. 8(1). 151–151. 33 indexed citations
7.
Rezende, Mariana T., Andrea Bianchi, Cláudia Martins Carneiro, et al.. (2021). A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification. Applied Sciences. 11(9). 4091–4091. 24 indexed citations
8.
Rezende, Mariana T., Andrea Bianchi, Cláudia Martins Carneiro, et al.. (2021). A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification. Journal of Imaging. 7(7). 111–111. 38 indexed citations
9.
Bianchi, Andrea, et al.. (2021). An ensemble method for nuclei detection of overlapping cervical cells. Expert Systems with Applications. 185. 115642–115642. 13 indexed citations
10.
Araújo, Flávio H. D., Romuere Silva, Daniela Ushizima, et al.. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics. 72. 13–21. 87 indexed citations
11.
Ushizima, Daniela, et al.. (2019). Neural Networks Predict Fluid Dynamics Solutions from Tiny Datasets.. arXiv (Cornell University). 6 indexed citations
12.
Ramalho, Geraldo L. B., Mariana T. Rezende, F.N.S. Medeiros, et al.. (2019). Saliency-driven system models for cell analysis with deep learning. Computer Methods and Programs in Biomedicine. 182. 105053–105053. 4 indexed citations
13.
Williams, Teresa E., Daniela Ushizima, Chenhui Zhu, et al.. (2017). Nearest-neighbour nanocrystal bonding dictates framework stability or collapse in colloidal nanocrystal frameworks. Chemical Communications. 53(35). 4853–4856. 6 indexed citations
14.
Perciano, Talita, Daniela Ushizima, Harinarayan Krishnan, Dilworth Y. Parkinson, & James A. Sethian. (2017). FibriPy: a software environment for fiber analysis from 3D micro-computed tomography data. eScholarship (California Digital Library). 1(2017). 25–28. 1 indexed citations
15.
Nguy, Austin, et al.. (2017). Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding. Journal of Neuroscience Methods. 282. 20–33. 18 indexed citations
16.
Venkatakrishnan, Singanallur, K. Aditya Mohan, K. Beattie, et al.. (2016). Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible. Electronic Imaging. 28(19). 1–7. 7 indexed citations
17.
Ushizima, Daniela, Dmitriy Morozov, Gunther H. Weber, et al.. (2012). Augmented Topological Descriptors of Pore Networks for Material Science. IEEE Transactions on Visualization and Computer Graphics. 18(12). 2041–2050. 36 indexed citations
18.
Ushizima, Daniela. (2010). SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging. University of North Texas Digital Library (University of North Texas).
19.
Ushizima, Daniela, et al.. (2010). Vessel network detection using contour evolution and color components. PubMed. 2010. 3129–3132. 2 indexed citations
20.
Marques, Régis C. P., F.N.S. Medeiros, & Daniela Ushizima. (2009). Target Detection in SAR Images Based on a Level Set Approach. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 39(2). 214–222. 18 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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