World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4623
World Ranking
10915
National Ranking
38

Marcelo Gattass publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Marcelo Gattass sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 207 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Marcelo Gattass D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Marcelo Gattass sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Marcelo Gattass is affiliated with the Pontifical Catholic University of Rio de Janeiro in Brazil. Their research spans multiple fields, including Engineering, Computer Science, and Medicine, with a notable focus on areas such as Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, and Ocean Engineering.

Their work encompasses significant topics such as Reservoir Engineering and Simulation Methods, Radiomics and Machine Learning in Medical Imaging, Seismic Imaging and Inversion Techniques, AI in cancer detection, Advanced Neural Network Applications, 3D Shape Modeling and Analysis, and Hydrocarbon exploration and reservoir analysis.

Marcelo Gattass has contributed to several recent papers, demonstrating a focus on medical imaging and computational methods. These papers include:

  • "Kidney segmentation from computed tomography images using deep neural network," 2020, Computers in Biology and Medicine
  • "Breast cancer diagnosis from histopathological images using textural features and CBIR," 2020, Artificial Intelligence in Medicine
  • "Crowd-SLAM: Visual SLAM Towards Crowded Environments using Object Detection," 2021, Journal of Intelligent & Robotic Systems
  • "Kidney tumor segmentation from computed tomography images using DeepLabv3+ 2.5D model," 2021, Expert Systems with Applications
  • "Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost," 2021, Expert Systems with Applications

Frequent collaborators with Marcelo Gattass include Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, João Otávio Bandeira Diniz, Luana Batista da Cruz, and João Dallyson Sousa de Almeida. These coauthors have contributed to several joint publications.

The scientist's research is published mainly in specialized venues that include:

  • Expert Systems with Applications
  • Computers in Biology and Medicine
  • Journal of Intelligent & Robotic Systems
  • Multimedia Tools and Applications
  • Computers & Geosciences

Best Publications

  • Convolutional neural network-based PSO for lung nodule false positive reduction on CT images.

    Giovanni Lucca França da Silva;Thales Levi Azevedo Valente;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

  • Detection of masses in mammogram images using CNN, geostatistic functions and SVM

    Wener Borges Sampaio;Edgar Moraes Diniz;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

  • TerraLib: Technology in Support of GIS Innovation

    Gilberto Câmara;Ricardo Cartaxo Modesto;Bianca Maria Pedrosa;Lúbia Vinhas

  • Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data

    Augusto Cunha;Axelle Pochet;Hélio Lopes;Marcelo Gattass

  • Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine

    Leonardo de Oliveira Martins;Geraldo Braz Junior;Aristófanes Correa Silva;Anselmo Cardoso de Paiva

  • Automatic segmentation of lung nodules with growing neural gas and support vector machine

    Stelmo Magalhães Barros Netto;Aristófanes Corrêa Silva;Rodolfo Acatauassú Nunes;Marcelo Gattass

  • Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index.

    Antonio Oseas de Carvalho Filho;Wener Borges de Sampaio;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

  • Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

    Alex Martins Santos;Antonio Oseas de Carvalho Filho;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

  • Kidney segmentation from computed tomography images using deep neural network.

    Luana Batista da Cruz;José Denes Lima Araújo;Jonnison Lima Ferreira;João Otávio Bandeira Diniz

  • Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM

    Fernando Soares Sérvulo de Oliveira;Antonio Oseas de Carvalho Filho;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

  • Kidney tumor segmentation from computed tomography images using DeepLabv3+ 2.5D model

    Unknown

  • Breast cancer diagnosis from histopathological images using textural features and CBIR.

    Edson Damasceno Carvalho;Antonio Oseas de Carvalho Filho;Romuere R. V. e Silva;Flávio H. D. Araújo

  • Liver segmentation from computed tomography images using cascade deep learning

    Unknown

  • Node and element resequencing using the Laplacian of a finite element graph: Part I—General concepts and algorithm

    Glaucio H. Paulino;Ivan F. M. Menezes;Marcelo Gattass;Subrata Mukherjee

  • Lung nodules diagnosis based on evolutionary convolutional neural network

    Giovanni L. Silva;Otílio P. Silva Neto;Aristófanes C. Silva;Anselmo C. Paiva

  • Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

    Axelle Pochet;Pedro H. B. Diniz;Helio Lopes;Marcelo Gattass

  • Crowd-SLAM: Visual SLAM Towards Crowded Environments using Object Detection

    João Carlos Virgolino Soares;Marcelo Gattass;Marco Antonio Meggiolaro

  • Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma

    Marcos Vinícius dos Santos Ferreira;Antonio Oseas de Carvalho Filho;Alcilene Dalília de Sousa;Aristófanes Corrêa Silva

  • Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks

    João Otávio Bandeira Diniz;Pedro Henrique Bandeira Diniz;Thales Levi Azevedo Valente;Aristófanes Corrêa Silva

  • INTERSECTING AND TRIMMING PARAMETRIC MESHES ON FINITE-ELEMENT SHELLS

    Luiz Cristovão G. Coelho;Marcelo Gattass;Luiz Henrique de Figueiredo

  • Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network

    Antonio Oseas de Carvalho Filho;Aristofanes Corrêa Silva;Anselmo Cardoso de Paiva;Rodolfo Acatauassú Nunes

  • Automatic embryonic stem cells detection and counting method in fluorescence microscopy images

    Geisa M. Faustino;Marcelo Gattass;Stevens Rehen;Carlos J. P. de Lucena

  • Lung nodule classification using artificial crawlers, directional texture and support vector machine

    Bruno Rodrigues Froz;Antonio Oseas de Carvalho Filho;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva

Frequent Co-Authors

Aristófanes Corrêa Silva
Aristófanes Corrêa Silva Federal University of Maranhão
Glaucio H. Paulino
Glaucio H. Paulino Princeton University
Carlos José Pereira de Lucena
Carlos José Pereira de Lucena Pontifical Catholic University of Rio de Janeiro
Luiz Velho
Luiz Velho Instituto Nacional de Matemática Pura e Aplicada
Subrata Mukherjee
Subrata Mukherjee Cornell University
Stevens K. Rehen
Stevens K. Rehen Federal University of Rio de Janeiro
Marley M. B. R. Vellasco
Marley M. B. R. Vellasco Pontifical Catholic University of Rio de Janeiro

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