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
Computer Science D-index 54 Citations 10,380 214 World Ranking 2263 National Ranking 1226

Research.com Recognitions

Awards & Achievements

2019 - SPIE Fellow

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

His primary areas of study are Artificial intelligence, Mammography, Pattern recognition, Linear discriminant analysis and Computer-aided diagnosis. His Artificial intelligence study often links to related topics such as Receiver operating characteristic. As part of one scientific family, Berkman Sahiner deals mainly with the area of Mammography, narrowing it down to issues related to the Computer vision, and often Mammary gland.

The Pattern recognition study combines topics in areas such as Covariance and Sample size determination. His work deals with themes such as Classifier, Linear classifier, Pixel and Feature, which intersect with Linear discriminant analysis. His Computer-aided diagnosis research is multidisciplinary, incorporating perspectives in Digital mammography, Nuclear medicine, Data set and Medical imaging.

His most cited work include:

  • Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images (317 citations)
  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system (282 citations)
  • A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis (254 citations)

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

Berkman Sahiner mostly deals with Artificial intelligence, Computer-aided diagnosis, Computer vision, Mammography and Pattern recognition. Artificial intelligence and Receiver operating characteristic are frequently intertwined in his study. Berkman Sahiner focuses mostly in the field of Receiver operating characteristic, narrowing it down to topics relating to Artificial neural network and, in certain cases, Convolutional neural network.

As a part of the same scientific family, Berkman Sahiner mostly works in the field of Computer-aided diagnosis, focusing on Image segmentation and, on occasion, Thresholding. In his study, Pathology is inextricably linked to Medical imaging, which falls within the broad field of Mammography. His Pattern recognition research incorporates themes from Contextual image classification and Image processing.

He most often published in these fields:

  • Artificial intelligence (65.76%)
  • Computer-aided diagnosis (40.00%)
  • Computer vision (34.24%)

What were the highlights of his more recent work (between 2013-2021)?

  • Artificial intelligence (65.76%)
  • Pattern recognition (33.22%)
  • Receiver operating characteristic (22.71%)

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

Berkman Sahiner mainly investigates Artificial intelligence, Pattern recognition, Receiver operating characteristic, Imaging phantom and Algorithm. His Artificial intelligence study incorporates themes from Machine learning and Computer vision. His Pattern recognition research is multidisciplinary, incorporating elements of False positive paradox, Mean squared error, Full field, Voxel and Prostate cancer.

His research integrates issues of Sample size determination, Data mining, Precision and recall, Classifier and Digital mammography in his study of Receiver operating characteristic. His Digital mammography study improves the overall literature in Mammography. His study explores the link between Mammography and topics such as Medical imaging that cross with problems in Cancer prevalence and Standard error.

Between 2013 and 2021, his most popular works were:

  • Deep learning in medical imaging and radiation therapy. (208 citations)
  • 3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT. (54 citations)
  • Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks. (33 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Receiver operating characteristic and Algorithm. His Artificial intelligence research focuses on Segmentation, Medical imaging, Convolutional neural network, Data set and Overfitting. Berkman Sahiner combines subjects such as Breast imaging and Mammography with his study of Data set.

Berkman Sahiner has researched Pattern recognition in several fields, including Abdominal ct, Computed tomography and Colitis. His studies in Receiver operating characteristic integrate themes in fields like Classifier, Measure, Image processing, Feature selection and Thoracic ct. Berkman Sahiner has included themes like Imaging phantom, Nuclear medicine and Generalization in his Algorithm study.

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

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

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 Physics (2002)

397 Citations

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

Yiheng Zhang;Heang Ping Chan;Berkman Sahiner;Jun Wei.
Medical Physics (2006)

364 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

An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

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

300 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

Deep learning in medical imaging and radiation therapy.

Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang.
Medical Physics (2019)

260 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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