Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
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9781510633995, 9781510634008

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Front Matter: Volume 11316

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2570845 ◽

2020 ◽

Keyword(s):

Matter Volume

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Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation)

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2556704 ◽

2020 ◽

Author(s):

Maryellen L. Giger

Keyword(s):

Medical Imaging

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Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on read-data or conjectured Obuchowski-Rockette parameter estimates

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2550541 ◽

2020 ◽

Author(s):

Stephen L. Hillis

Keyword(s):

Parameter Estimates ◽

Model Parameters ◽

Rating Data ◽

Disease Rating

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Sequential reading effects in Dutch screening mammography

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2549320 ◽

2020 ◽

Author(s):

Craig K. Abbey ◽

Michael A. Webster ◽

Tanya Geertse ◽

Danielle van der Waal ◽

Eric Tetteroo ◽

...

Keyword(s):

Screening Mammography

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Anthropomorphic ResNet18 for multi-vendor DBT image quality evaluation

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2549000 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dimitar Petrov ◽

Nicholas Marshall ◽

Liesbeth Vancoillie ◽

Lesley Cockmartin ◽

Hilde Bosmans

Keyword(s):

Image Quality ◽

Quality Evaluation ◽

Image Quality Evaluation

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Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2550601 ◽

2020 ◽

Author(s):

Dylan H. Shekter ◽

Frank W. Samuelson

Keyword(s):

Roc Curves ◽

Finite Samples

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Deep learning-based model observers that replicate human observers for PET imaging

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2547505 ◽

2020 ◽

Cited By ~ 1

Author(s):

Fenglei Fan ◽

Sangtae Ahn ◽

Bruno De Man ◽

Kristen A. Wangerin ◽

Scott D. Wollenweber ◽

...

Keyword(s):

Deep Learning ◽

Pet Imaging ◽

Model Observers

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Investigating the potential of a gist-sensitive computer-aided detection tool

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2549921 ◽

2020 ◽

Author(s):

Ziba Gandomkar ◽

Ernest Ekpo ◽

Sarah Lewis ◽

Moayyad Suleiman ◽

Kriscia Tapia ◽

...

Keyword(s):

Computer Aided Detection ◽

Detection Tool ◽

Computer Aided

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Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2550538 ◽

2020 ◽

Author(s):

Kenny H. Cha ◽

Alexej Gossmann ◽

Nicholas Petrick ◽

Berkman Sahiner

Keyword(s):

Machine Learning ◽

Machine Learning Classifiers ◽

Learning Classifiers

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Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2549550 ◽

2020 ◽

Author(s):

Changwoo Lee ◽

Jongduk Baek

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Breast Tomosynthesis ◽

Model Observer

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Medical Imaging 2020: Image Perception, Observer Performance, and Technology AssessmentLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By SPIE

Front Matter: Volume 11316

Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation)

Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on read-data or conjectured Obuchowski-Rockette parameter estimates

Sequential reading effects in Dutch screening mammography

Anthropomorphic ResNet18 for multi-vendor DBT image quality evaluation

Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples

Deep learning-based model observers that replicate human observers for PET imaging

Investigating the potential of a gist-sensitive computer-aided detection tool

Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help

Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Latest Publications