World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8828
World Ranking
7168
National Ranking
3139

Ulas Bagci 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 Ulas Bagci 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: 232 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Ulas Bagci 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 Ulas Bagci 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Ulas Bagci is affiliated with Northwestern University in the United States. Their research activity spans primarily the fields of Medicine and Computer Science, with a strong focus on subfields including Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Oncology, and Biomedical Engineering.

Their scientific contributions emphasize topics such as Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, COVID-19 diagnosis using AI, Advanced Neural Network Applications, Brain Tumor Detection and Classification, Medical Image Segmentation Techniques, and Colorectal Cancer Screening and Detection.

Key recent publications include:

  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets, 2020, Nature Communications
  • Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions, 2023, IEEE Internet of Things Journal
  • TGANet: Text-Guided Attention for Improved Polyp Segmentation, 2022, Lecture notes in computer science
  • EEG Based Classification of Long-Term Stress Using Psychological Labeling, 2020, Sensors
  • The Impact of COVID-19 on African American Communities in the United States, 2020, Health Equity

The scientist frequently publishes in venues such as arXiv (Cornell University), Lecture notes in computer science, Gastroenterology, Medical Image Analysis, and bioRxiv (Cold Spring Harbor Laboratory).

Frequent co-authors collaborating with Ulas Bagci include:

  • Debesh Jha
  • Görkem Durak
  • Elif Keleş
  • Yury Velichko
  • Nikhil Kumar Tomar

Best Publications

  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

    Stephanie A. Harmon;Thomas H. Sanford;Sheng Xu;Evrim B. Turkbey

  • A review on segmentation of positron emission tomography images

    Brent Foster;Ulas Bagci;Awais Mansoor;Ziyue Xu

  • Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

    Xiahai Zhuang;Lei Li;Christian Payer;Darko Stern

  • Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

    Awais Mansoor;Ulas Bagci;Brent Foster;Ziyue Xu

  • Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

    Mingchen Gao;Ulas Bagci;Le Lu;Aaron Wu

  • Capsules for Object Segmentation

    Rodney LaLonde;Ulas Bagci

  • Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models

    Xinjian Chen;J. K. Udupa;U. Bagci;Ying Zhuge

  • Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

    Sarfaraz Hussein;Pujan Kandel;Candice W. Bolan;Michael B. Wallace

  • RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

    Hrvoje Bogunovic;Freerk Venhuizen;Sophie Klimscha;Stefanos Apostolopoulos

  • How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis

    Maria J. M. Chuquicusma;Sarfaraz Hussein;Jeremy Burt;Ulas Bagci

  • A Generic Approach to Pathological Lung Segmentation

    Awais Mansoor;Ulas Bagci;Ziyue Xu;Brent Foster

  • Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8

    Unknown

  • Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

    Ulas Bagci;Jayaram K. Udupa;Neil Mendhiratta;Neil Mendhiratta;Brent Foster

  • Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

    Jeremy R Burt;Jeremy R Burt;Neslisah Torosdagli;Naji Khosravan;Harish RaviPrakash

  • Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning

    Sarfaraz Hussein;Kunlin Cao;Qi Song;Ulas Bagci

  • Deep Geodesic Learning for Segmentation and Anatomical Landmarking

    Neslisah Torosdagli;Denise K. Liberton;Payal Verma;Murat Sincan

  • Quality assurance of computer-aided detection and diagnosis in colonoscopy

    Daniela Guerrero Vinsard;Daniela Guerrero Vinsard;Yuichi Mori;Masashi Misawa;Shin ei Kudo

  • S4ND : Single-Shot Single-Scale Lung Nodule Detection

    Naji Khosravan;Ulas Bagci

  • TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

    Sarfaraz Hussein;Robert Gillies;Kunlin Cao;Qi Song

  • CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN

    Aliasghar Mortazi;Rashed Karim;Kawal S. Rhode;Jeremy Burt

  • EEG based Classification of Long-term Stress Using Psychological Labeling.

    Sanay Muhammad Umar Saeed;Syed Muhammad Anwar;Syed Muhammad Anwar;Humaira Khalid;Muhammad Majid

  • Supervised and Unsupervised Tumor Characterization in the Deep Learning Era.

    Sarfaraz Hussein;Maria M. J. Chuquicusma;Pujan Kandel;Candice W. Bolan

Frequent Co-Authors

Ziyue Xu
Ziyue Xu Nvidia (United States)
Li Bai
Li Bai University of Nottingham
Jayaram K. Udupa
Jayaram K. Udupa University of Pennsylvania
Xinjian Chen
Xinjian Chen Soochow University
Jianhua Yao
Jianhua Yao Tencent (China)
Baris Turkbey
Baris Turkbey National Institutes of Health
Concetto Spampinato
Concetto Spampinato University of Catania
William R. Bishai
William R. Bishai Johns Hopkins University
Peter L. Choyke
Peter L. Choyke National Institutes of Health

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