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Computer Science
Australia
2025

D-Index & Metrics

Computer Science

D-Index
64
Citations
18577
World Ranking
2573
National Ranking
147

Gustavo Carneiro 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 Gustavo Carneiro 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 294 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Gustavo Carneiro 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 Gustavo Carneiro sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in Australia Leader Award
  • 2023 - Research.com Computer Science in Australia Leader Award
  • 2022 - Research.com Computer Science in Australia Leader Award

Overview

Gustavo Carneiro is affiliated with the University of Adelaide in Australia and has a significant body of work spanning computer science and medicine. Their research primarily focuses on artificial intelligence and its applications in medical imaging and cancer detection, with an emphasis on machine learning and data classification methodologies.

The scientist has contributed to 261 publications in computer science and 191 publications in medicine. Within these fields, the most prominent subfields of study include:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Oncology
  • Civil and Structural Engineering

Gustavo Carneiro's main research topics encompass:

  • Machine Learning and Data Classification
  • COVID-19 diagnosis using AI
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Pattern Recognition
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging

Among recent papers authored or co-authored by Gustavo Carneiro are:

  • "Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning," 2021, presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation," 2022, presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis," 2021, published in BMC Cancer
  • "ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification," 2022, presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Deep One-Class Classification via Interpolated Gaussian Descriptor," 2022, presented at the Proceedings of the AAAI Conference on Artificial Intelligence

Gustavo Carneiro frequently collaborates with a consistent group of co-authors, including:

  • Yuyuan Liu
  • Fengbei Liu
  • Chong Wang
  • Vasileios Belagiannis
  • Thanh-Toan Do

Best Publications

  • Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

    Ravi Garg;B. G. Vijay Kumar;Gustavo Carneiro;Ian D. Reid

  • Supervised Learning of Semantic Classes for Image Annotation and Retrieval

    G. Carneiro;A.B. Chan;P.J. Moreno;N. Vasconcelos

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

    M. Jorge Cardoso;Tal Arbel;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

    Ravi Garg;Vijay Kumar Bg;Gustavo Carneiro;Ian Reid

  • Weakly-Supervised Video Anomaly Detection With Robust Temporal Feature Magnitude Learning

    Yu Tian;Guansong Pang;Yuanhong Chen;Rajvinder Singh

  • Multi-modal Cycle-Consistent Generalized Zero-Shot Learning

    Rafael Felix;B. G. Vijay Kumar;Ian D. Reid;Gustavo Carneiro

  • Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

    Tuan Anh Ngo;Zhi Lu;Gustavo Carneiro

  • A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Smart Mining for Deep Metric Learning

    Ben Harwood;Vijay Kumar B. G;Gustavo Carneiro;Ian Reid

  • Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

    Unknown

  • Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions

    Vijay Kumar B G;Gustavo Carneiro;Ian Reid

  • Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

    Luke Oakden-Rayner;Jared Dunnmon;Gustavo Carneiro;Christopher Re

  • Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models

    Gustavo Carneiro;Jacinto C. Nascimento;Andrew P. Bradley

  • An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells

    Zhi Lu;Gustavo Carneiro;Andrew P. Bradley

  • Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

    G. Carneiro;B. Georgescu;S. Good;D. Comaniciu

  • Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Cross-layer design in 4G wireless terminals

    G. Carneiro;J. Ruela;M. Ricardo

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

    Danail Stoyanov;Zeike Taylor;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • The Segmentation of the Left Ventricle of the Heart From Ultrasound Data Using Deep Learning Architectures and Derivative-Based Search Methods

    G. Carneiro;J. C. Nascimento;A. Freitas

  • Formulating semantic image annotation as a supervised learning problem

    G. Carneiro;N. Vasconcelos

  • Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume

    Adrian Johnston;Gustavo Carneiro

  • Deep Learning and Convolutional Neural Networks for Medical Image Computing

    Le Lu;Yefeng Zheng;Gustavo Carneiro;Lin Yang

  • Deep Learning and Data Labeling for Medical Applications

    Gustavo Carneiro;Diana Mateus;Loïc Peter;Andrew Bradley

  • Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions

    Vijay Kumar B G;Gustavo Carneiro;Ian Reid

Frequent Co-Authors

Andrew P. Bradley
Andrew P. Bradley Queensland University of Technology
Ian Reid
Ian Reid University of Adelaide
Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Nuno Vasconcelos
Nuno Vasconcelos University of California, San Diego
João Paulo Papa
João Paulo Papa Sao Paulo State University
Allan D. Jepson
Allan D. Jepson University of Toronto
João Manuel R. S. Tavares
João Manuel R. S. Tavares University of Porto
Nassir Navab
Nassir Navab Technical University of Munich
Bogdan Georgescu
Bogdan Georgescu Princeton University
Tat-Jun Chin
Tat-Jun Chin University of Adelaide

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