Helena R. Torres

647 total citations
47 papers, 302 citations indexed

About

Helena R. Torres is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Helena R. Torres has authored 47 papers receiving a total of 302 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 13 papers in Biomedical Engineering and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Helena R. Torres's work include AI in cancer detection (9 papers), Craniofacial Disorders and Treatments (9 papers) and Medical Image Segmentation Techniques (7 papers). Helena R. Torres is often cited by papers focused on AI in cancer detection (9 papers), Craniofacial Disorders and Treatments (9 papers) and Medical Image Segmentation Techniques (7 papers). Helena R. Torres collaborates with scholars based in Portugal, Germany and Belgium. Helena R. Torres's co-authors include Bruno Oliveira, João L. Vilaça, Pedro Morais, Jaime C. Fonseca, Sandro Queirós, Mario Rüdiger, Fernando Veloso, Cahit Birdir, Estêvão Lima and Victor Alves and has published in prestigious journals such as Scientific Reports, Sensors and Medical Image Analysis.

In The Last Decade

Helena R. Torres

38 papers receiving 294 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Helena R. Torres Portugal 10 140 106 83 69 47 47 302
Bruno Oliveira Portugal 11 172 1.2× 246 2.3× 145 1.7× 108 1.6× 113 2.4× 55 528
Mostafa Ghelich Oghli Iran 9 97 0.7× 237 2.2× 107 1.3× 98 1.4× 39 0.8× 19 386
Giacomo Tarroni Italy 10 61 0.4× 172 1.6× 70 0.8× 59 0.9× 21 0.4× 30 377
Tao Zhong China 11 75 0.5× 233 2.2× 131 1.6× 79 1.1× 31 0.7× 36 462
Mousumi Bhaduri Canada 12 123 0.9× 158 1.5× 71 0.9× 75 1.1× 18 0.4× 20 381
Kristin McLeod Norway 9 78 0.6× 106 1.0× 53 0.6× 64 0.9× 59 1.3× 17 331
Ruobing Huang China 10 88 0.6× 162 1.5× 33 0.4× 167 2.4× 16 0.3× 29 316
Amir Alansary United States 9 121 0.9× 119 1.1× 59 0.7× 80 1.2× 18 0.4× 17 319
Benjamin Hou United Kingdom 8 77 0.6× 131 1.2× 51 0.6× 111 1.6× 16 0.3× 16 310
Sophia Bano United Kingdom 12 157 1.1× 49 0.5× 108 1.3× 64 0.9× 32 0.7× 44 412

Countries citing papers authored by Helena R. Torres

Since Specialization
Citations

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

Fields of papers citing papers by Helena R. Torres

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Helena R. Torres

This figure shows the co-authorship network connecting the top 25 collaborators of Helena R. Torres. A scholar is included among the top collaborators of Helena R. Torres 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 Helena R. Torres. Helena R. Torres 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.
Torres, Helena R., Bruno Oliveira, Cahit Birdir, et al.. (2024). Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data. IEEE Journal of Biomedical and Health Informatics. 28(12). 7287–7299. 1 indexed citations
3.
Queirós, Sandro, Helena R. Torres, Bruno Oliveira, et al.. (2024). Exploring optical flow inclusion into nnU-Net framework for surgical instrument segmentation. arXiv (Cornell University). 78–78.
4.
Morais, Pedro, et al.. (2024). Body Fluid Collection Devices for Ostomy Patients: A Review. Healthcare. 12(21). 2175–2175. 1 indexed citations
5.
Torres, Helena R., Bruno Oliveira, Pedro Morais, et al.. (2024). Infant head and brain segmentation from magnetic resonance images using fusion-based deep learning strategies. Multimedia Systems. 30(2). 2 indexed citations
6.
Morais, Pedro, et al.. (2023). Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum. Sensors. 23(9). 4427–4427.
7.
Vilaça, João L., et al.. (2023). Smart scan of medical device displays to integrate with a mHealth application. Heliyon. 9(6). e16297–e16297. 2 indexed citations
8.
Oliveira, Bruno, Helena R. Torres, Pedro Morais, et al.. (2023). A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Scientific Reports. 13(1). 761–761. 14 indexed citations
9.
Torres, Helena R., et al.. (2022). ECG classification using Artificial Intelligence: Model Optimization and Robustness Assessment. 1–8. 2 indexed citations
10.
Oliveira, Bruno, et al.. (2022). Ultrasound training simulator using augmented reality glasses: an accuracy and precision assessment study. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 4461–4464. 10 indexed citations
11.
Torres, Helena R., et al.. (2022). Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 3878–3881. 8 indexed citations
12.
Torres, Helena R., et al.. (2022). Deep learning methods for lesion detection on mammography images: a comparative analysis. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 3526–3529. 7 indexed citations
13.
Torres, Helena R., Bruno Oliveira, Pedro Morais, et al.. (2022). Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities. Journal of Biomedical Informatics. 132. 104121–104121. 1 indexed citations
14.
Veloso, Fernando, D. Miranda, Pedro Morais, et al.. (2022). Study of the compression behavior of functionally graded lattice for customized cranial remodeling orthosis. Journal of the mechanical behavior of biomedical materials. 130. 105191–105191. 3 indexed citations
15.
Torres, Helena R., Pedro Morais, Bruno Oliveira, et al.. (2022). Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability. Annals of Biomedical Engineering. 50(9). 1022–1037. 4 indexed citations
16.
Torres, Helena R., Sandro Queirós, Pedro Morais, et al.. (2020). Kidney Segmentation in 3-D Ultrasound Images Using a Fast Phase-Based Approach. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 68(5). 1521–1531. 10 indexed citations
17.
Queirós, Sandro, et al.. (2020). A system for the generation of in-car human body pose datasets. Machine Vision and Applications. 32(1). 9 indexed citations
18.
Rodrigues, Nuno F., et al.. (2019). Top-Down Human Pose Estimation with Depth Images and Domain Adaptation. 281–288. 1 indexed citations
19.
Torres, Helena R., Sandro Queirós, Pedro Morais, et al.. (2018). Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review. Computer Methods and Programs in Biomedicine. 157. 49–67. 69 indexed citations
20.
Torres, Helena R., Bruno Oliveira, Sandro Queirós, et al.. (2016). Kidney segmentation in 3D CT images using B-Spline Explicit Active Surfaces. RepositóriUM (Universidade do Minho). 18. 1–7. 4 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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