D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Medicine D-index 70 Citations 13,113 192 World Ranking 15488 National Ranking 8032

Overview

What is he best known for?

The fields of study he is best known for:

  • Cancer
  • Mammography
  • Radiology

Mark A. Helvie focuses on Mammography, Artificial intelligence, Pattern recognition, Radiology and Computer-aided diagnosis. Many of his research projects under Mammography are closely connected to CAD with CAD, tying the diverse disciplines of science together. His work in Artificial intelligence addresses subjects such as Receiver operating characteristic, which are connected to disciplines such as Classifier and Wavelet.

His study in Pattern recognition is interdisciplinary in nature, drawing from both Film mammography and Data set. His Radiology research is multidisciplinary, incorporating perspectives in Predictive value of tests and Invasive lobular carcinoma. His work carried out in the field of Computer-aided diagnosis brings together such families of science as Nuclear medicine, Digital mammography and Medical imaging.

His most cited work include:

  • Metabolic monitoring of breast cancer chemohormonotherapy using positron emission tomography: initial evaluation. (534 citations)
  • Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images (317 citations)
  • Breast cancer screening and diagnosis: Clinical practice guidelines in oncology™ (265 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Mammography, Artificial intelligence, Breast cancer, Radiology and Pattern recognition. His work in Mammography addresses issues such as Biopsy, which are connected to fields such as Mastectomy. His Artificial intelligence study integrates concerns from other disciplines, such as Digital mammography, Computer vision and Receiver operating characteristic.

His Breast cancer research includes themes of Gynecology and Oncology. The Radiology study combines topics in areas such as Malignancy, Retrospective cohort study, Carcinoma and Mammary gland. The study incorporates disciplines such as Contextual image classification and Pixel in addition to Pattern recognition.

He most often published in these fields:

  • Mammography (56.49%)
  • Artificial intelligence (36.49%)
  • Breast cancer (31.93%)

What were the highlights of his more recent work (between 2014-2021)?

  • Mammography (56.49%)
  • Breast cancer (31.93%)
  • Artificial intelligence (36.49%)

In recent papers he was focusing on the following fields of study:

Mark A. Helvie mainly investigates Mammography, Breast cancer, Artificial intelligence, Pattern recognition and Digital mammography. His studies deal with areas such as Gynecology, Radiology and Mass screening as well as Mammography. The Biopsy research Mark A. Helvie does as part of his general Radiology study is frequently linked to other disciplines of science, such as Upgrade, therefore creating a link between diverse domains of science.

As a part of the same scientific study, Mark A. Helvie usually deals with the Breast cancer, concentrating on Oncology and frequently concerns with Overdiagnosis. His Artificial intelligence research focuses on subjects like Digital Breast Tomosynthesis, which are linked to Microcalcification clusters, Computer vision, Computer-aided diagnosis, Bilateral filter and Nuclear medicine. While the research belongs to areas of Pattern recognition, Mark A. Helvie spends his time largely on the problem of Cluster analysis, intersecting his research to questions surrounding Embedding, Histogram and Magnetic resonance imaging.

Between 2014 and 2021, his most popular works were:

  • Breast Cancer Screening and Diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. (216 citations)
  • Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography (138 citations)
  • Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging (90 citations)

In his most recent research, the most cited papers focused on:

  • Cancer
  • Internal medicine
  • Mammography

His scientific interests lie mostly in Mammography, Breast cancer, Artificial intelligence, Convolutional neural network and Pattern recognition. To a larger extent, Mark A. Helvie studies Cancer with the aim of understanding Mammography. His Cancer study combines topics in areas such as Biopsy and Radiology.

His Screening mammography study, which is part of a larger body of work in Breast cancer, is frequently linked to Genome-wide association study, bridging the gap between disciplines. Mark A. Helvie has researched Artificial intelligence in several fields, including Digital Breast Tomosynthesis and Digital mammography. His studies deal with areas such as Transfer of learning, Computer-aided diagnosis, Deep learning and Test set as well as Convolutional neural network.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Metabolic monitoring of breast cancer chemohormonotherapy using positron emission tomography: initial evaluation.

Richard L. Wahl;Ken Zasadny;Mark Helvie;Gary D. Hutchins.
Journal of Clinical Oncology (1993)

711 Citations

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

B. Sahiner;Heang-Ping Chan;N. Petrick;Datong Wei.
IEEE Transactions on Medical Imaging (1996)

473 Citations

Breast Cancer Screening and Diagnosis

Therese B. Bevers;Benjamin O. Anderson;Ermelinda Bonaccio;Sandra Buys.
Journal of The National Comprehensive Cancer Network (2009)

421 Citations

Breast cancer screening and diagnosis: Clinical practice guidelines in oncology™

Therese B. Bevers;Benjamin O. Anderson;Ermelinda Bonaccio;Patrick I. Borgen.
Journal of The National Comprehensive Cancer Network (2009)

397 Citations

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

Heang-Ping Chan;Berkman Sahiner;Mark A. Helvie;Nicholas Petrick.
Radiology (1999)

324 Citations

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Heang Ping Chan;Berkman Sahiner;Kwok Leung Lam;Nicholas Petrick.
Medical Physics (1998)

298 Citations

Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

Heang-Ping Chan;Datong Wei;Mark A. Helvie;Berkman Sahiner.
Physics in Medicine and Biology (1995)

294 Citations

Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

Berkman Sahiner;Heang Ping Chan;Nicholas Petrick;Mark A. Helvie.
Medical Physics (1998)

288 Citations

Changes in surgical management resulting from case review at a breast cancer multidisciplinary tumor board

Erika A. Newman;Amy B. Guest;Mark A. Helvie;Marilyn A. Roubidoux.
Cancer (2006)

267 Citations

Computerized image analysis: estimation of breast density on mammograms.

Chuan Zhou;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie.
Medical Physics (2001)

255 Citations

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