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D-Index
67
Citations
14979
World Ranking
1328
National Ranking
434

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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