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D-Index & Metrics

Engineering and Technology

D-Index
67
Citations
14979
World Ranking
1329
National Ranking
435

Lubomir M. Hadjiiski publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Lubomir M. Hadjiiski sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 372 publications — 86th percentile

86% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Lubomir M. Hadjiiski D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Lubomir M. Hadjiiski sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 67 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Lubomir M. Hadjiiski is affiliated with the University of Michigan-Ann Arbor in the United States. Their research focuses primarily on medicine, with extensive work in radiology, nuclear medicine, and imaging. Additional subfields of study include pulmonary and respiratory medicine, artificial intelligence, biomedical engineering, and surgery.

Their recent publications cover multiple aspects of medical imaging and artificial intelligence applications in healthcare. Notable papers include:

  • "Deep Learning in Medical Image Analysis," 2020, published in Advances in Experimental Medicine and Biology
  • "Computer-aided diagnosis in the era of deep learning," 2020, published in Medical Physics
  • "AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging," 2022, published in Medical Physics
  • "Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets," 2020, published in Tomography
  • "Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI," 2020, published in Medical Imaging 2020: Computer-Aided Diagnosis

Frequent coauthors who have collaborated with Hadjiiski include:

  • Heang-Ping Chan
  • Ravi K. Samala
  • Chuan Zhou
  • Karen Drukker
  • Samuel G. Armato

The scientist publishes regularly in several venues, with recurring appearances in:

  • Medical Physics
  • Tomography
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • Cancers
  • BJR|Artificial Intelligence

The main research themes in Hadjiiski's work revolve around radiomics and machine learning in medical imaging, AI in cancer detection, and lung cancer diagnosis and treatment. Other topics of interest include artificial intelligence in healthcare and education, COVID-19 diagnosis using AI, advanced X-ray and CT imaging techniques, as well as bladder and urothelial cancer treatments.

This comprehensive portfolio reflects a multidisciplinary approach integrating clinical medicine, imaging technologies, and artificial intelligence methodologies.

Best Publications

  • Deep learning in medical imaging and radiation therapy.

    Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang

  • Deep Learning in Medical Image Analysis.

    Heang-Ping Chan;Ravi K. Samala;Lubomir M. Hadjiiski;Chuan Zhou

  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

    Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan

  • Medical Imaging 2015: Computer-Aided Diagnosis

    Lubomir M. Hadjiiski;Georgia D. Tourassi

  • A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis

    Yiheng Zhang;Heang Ping Chan;Berkman Sahiner;Jun Wei

  • Computer-aided diagnosis in the era of deep learning.

    Heang-Ping Chan;Lubomir M Hadjiiski;Ravi K Samala

  • Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

    Ted W. Way;Lubomir M. Hadjiiski;Berkman Sahiner;Heang Ping Chan

  • Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

    Ravi K. Samala;Heang Ping Chan;Lubomir Hadjiiski;Mark A. Helvie

  • Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

    Kenny H. Cha;Lubomir Hadjiiski;Ravi K. Samala;Heang Ping Chan

  • Computerized image analysis: estimation of breast density on mammograms.

    Chuan Zhou;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Improvement of mammographic mass characterization using spiculation measures and morphological features

    Berkman Sahiner;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets

    Ravi K. Samala;Heang-Ping Chan;Lubomir Hadjiiski;Mark A. Helvie

  • Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization

    B. Sahiner;N. Petrick;Heang-Ping Chan;L.M. Hadjiiski

  • Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

    Ravi K. Samala;Heang Ping Chan;Lubomir M. Hadjiiski;Mark A. Helvie

  • System and Method of Identifying a Potential Lung Nodule

    Heang-Ping Chan;Berkman Sahiner;Lubomir M. Hadjiyski;Chuan Zhou

  • Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.

    Kenny H. Cha;Lubomir Hadjiiski;Heang Ping Chan;Alon Z. Weizer

  • Digital Breast Tomosynthesis Is Comparable to Mammographic Spot Views for Mass Characterization

    Mitra Noroozian;Lubomir Hadjiiski;Sahand Rahnama-Moghadam;Katherine A. Klein

  • Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.

    Ted W. Way;Berkman Sahiner;Heang Ping Chan;Lubomir Hadjiiski

  • PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.

    Samuel G. Armato;Henkjan Huisman;Karen Drukker;Lubomir Hadjiiski

  • Computer-aided Detection System for Breast Masses on Digital Tomosynthesis Mammograms: Preliminary Experience

    Heang Ping Chan;Jun Wei;Berkman Sahiner;Elizabeth A. Rafferty

  • Evaluation of computer-aided detection and diagnosis systems

    Nicholas Petrick;Berkman Sahiner;Samuel G. Armato;Alberto Bert

  • Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

    Berkman Sahiner;Heang Ping Chan;Marilyn A. Roubidoux;Lubomir M. Hadjiiski

  • Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size.

    Berkman Sahiner;Heang Ping Chan;Nicholas Petrick;Robert F. Wagner

  • Effect of CAD on Radiologists’ Detection of Lung Nodules on Thoracic CT Scans: Analysis of an Observer Performance Study by Nodule Size

    Berkman Sahiner;Heang Ping Chan;Lubomir M. Hadjiiski;Philip N. Cascade

  • Dual system approach to computerâ aided detection of breast masses on mammograms

    Jun Wei;Heang‐ping Chan;Berkman Sahiner;Lubomir M. Hadjiiski

Frequent Co-Authors

Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Berkman Sahiner
Berkman Sahiner United States Food and Drug Administration
Jun Wei
Jun Wei Harbin Institute of Technology
Mark A. Helvie
Mark A. Helvie University of Michigan–Ann Arbor
Nicholas Petrick
Nicholas Petrick US Food and Drug Administration
Ella A. Kazerooni
Ella A. Kazerooni University of Michigan–Ann Arbor
Metin N. Gurcan
Metin N. Gurcan Wake Forest University
Paul L. Carson
Paul L. Carson University of Michigan–Ann Arbor
Philip K. Hopke
Philip K. Hopke Clarkson University
Daniel B. Kopans
Daniel B. Kopans Harvard University

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