World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
3750
World Ranking
10941
National Ranking
546

Isaac Shiri 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 Isaac Shiri 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: 72 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Isaac Shiri 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 Isaac Shiri 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

Isaac Shiri is affiliated with University Hospital Bern in Germany and has made contributions primarily in the field of Medicine, with a particular focus on Radiology, Nuclear Medicine, and Imaging.

Their research encompasses several subfields including Biomedical Engineering, Pulmonary and Respiratory Medicine, Cardiology and Cardiovascular Medicine, and Artificial Intelligence. The scientist's work covers multiple main topics, notably:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced X-ray and CT Imaging
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment
  • COVID-19 diagnosis using AI
  • Cardiac Imaging and Diagnostics
  • AI in cancer detection

The most frequent publication venues where their research appears include:

  • 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
  • European Journal of Nuclear Medicine and Molecular Imaging
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Computers in Biology and Medicine
  • European Heart Journal

Frequent collaborators in their research include Habib Zaidi, Ghasem Hajianfar, Yazdan Salimi, Hossein Arabi, and Arman Rahmim.

Selected recent papers authored or coauthored by Isaac Shiri are:

  • Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms, 2020, Molecular Imaging and Biology
  • The promise of artificial intelligence and deep learning in PET and SPECT imaging, 2021, Physica Medica
  • Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging, 2021, European Journal of Nuclear Medicine and Molecular Imaging
  • The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights, 2024, Radiology
  • Radiomics for classification of bone mineral loss: A machine learning study, 2020, Diagnostic and Interventional Imaging

Best Publications

  • The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies.

    Isaac Shiri;Arman Rahmim;Pardis Ghaffarian;Parham Geramifar

  • The promise of artificial intelligence and deep learning in PET and SPECT imaging.

    Hossein Arabi;Azadeh AkhavanAllaf;Amirhossein Sanaat;Isaac Shiri

  • MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography

    Shakiba Moradi;Mostafa Ghelich Oghli;Azin Alizadehasl;Isaac Shiri

  • Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches

    Isaac Shiri;Hassan Maleki;Ghasem Hajianfar;Hamid Abdollahi

  • Radiomics for classification of bone mineral loss: A machine learning study

    S. Rastegar;M. Vaziri;Y. Qasempour;M.R. Akhash

  • Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer

    Hamid Abdollahi;Bahram Mofid;Isaac Shiri;Isaac Shiri;Abolfazl Razzaghdoust

  • Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.

    Isaac Shiri;Majid Sorouri;Parham Geramifar;Mostafa Nazari

  • Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

    Mostafa Nazari;Isaac Shiri;Ghasem Hajianfar;Niki Oveisi

  • Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

    Mostafa Nazari;Isaac Shiri;Habib Zaidi;Habib Zaidi

  • Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study

    Hamid Abdollahi;Shayan Mostafaei;Susan Cheraghi;Isaac Shiri

  • Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

    Isaac Shiri;Pardis Ghafarian;Parham Geramifar;Kevin Ho-Yin Leung

  • Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.

    Reza Mohammadi;Iman Shokatian;Mohammad Salehi;Hossein Arabi

  • Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

    Isaac Shiri;Azadeh Akhavanallaf;Amirhossein Sanaat;Yazdan Salimi

  • Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature

    Zahra Khodabakhshi;Shayan Mostafaei;Hossein Arabi;Mehrdad Oveisi

  • Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

    Isaac Shiri;Kiarash AmirMozafari Sabet;Hossein Arabi;Mozhgan Pourkeshavarz;Mozhgan Pourkeshavarz

  • CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

    Shayan Mostafaei;Shayan Mostafaei;Hamid Abdollahi;Shiva Kazempour Dehkordi;Isaac Shiri

  • Noninvasive O6 Methylguanine-DNA Methyltransferase Status Prediction in Glioblastoma Multiforme Cancer Using Magnetic Resonance Imaging Radiomics Features: Univariate and Multivariate Radiogenomics Analysis

    Ghasem Hajianfar;Isaac Shiri;Hassan Maleki;Niki Oveisi

  • Artificial intelligence-driven assessment of radiological images for COVID-19.

    Yassine Bouchareb;Pegah Moradi Khaniabadi;Faiza Al Kindi;Humoud Al Dhuhli

  • Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test─retest and image registration analyses

    Isaac Shiri;Ghasem Hajianfar;Ahmad Sohrabi;Hamid Abdollahi

  • Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma.

    Mehdi Amini;Mehdi Amini;Mostafa Nazari;Isaac Shiri;Ghasem Hajianfar

  • Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

    Isaac Shiri;Hasan Maleki;Hasan Maleki;Ghasem Hajianfar;Hamid Abdollahi;Hamid Abdollahi

  • Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning

    Mostafa Nazari;Isaac Shiri;Ghasem Hajianfar;Niki Oveisi

Frequent Co-Authors

Arman Rahmim
Arman Rahmim University of British Columbia
Amir Kasaeian
Amir Kasaeian Tehran University of Medical Sciences
Mohammad Abdollahi
Mohammad Abdollahi Tehran University of Medical Sciences

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to many related disciplines with strong industry demand. If you’re interested in multiple fields or are considering complementary skills, it’s worth exploring online degree options that can broaden your knowledge and career prospects.

Engineering remains tightly linked to computer science, especially in areas like robotics and automation. Those wanting to expand their skillset may look into the mechanical engineering cost of education to find budget-friendly online options. Similarly, for those fascinated by fundamental science, discovering the cheapest online physics degree can be a smart way to enhance your analytical toolkit.

For students drawn to data and analytics, there are plenty of affordable data science degrees to consider. As computing and electronics often converge, checking the best online electrical engineering programs USA can also help identify top-rated, flexible pathways.

By exploring online degrees in these closely connected fields, you can tailor your education to suit evolving career trends while managing costs and scheduling flexibility.

Best Scientists Citing Isaac Shiri

Trending Scientists

Recently Published Articles