Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
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Published By SPIE

9781510616431, 9781510616448

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Front Matter: Volume 10577

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2323943 ◽

2018 ◽

Keyword(s):

Matter Volume

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Comparing salience detection algorithms in mammograms

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293787 ◽

2018 ◽

Author(s):

Kristina Landino ◽

Murray Loew

Keyword(s):

Detection Algorithms

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Learning to see

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2299599 ◽

2018 ◽

Author(s):

Richard B. Gunderman

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Analysis of visual search behaviour from experienced radiologists interpreting digital breast tomosynthesis (DBT) images: a pilot study

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293615 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yan Chen ◽

Alastair Gale ◽

Leng Dong ◽

Qiang Tang ◽

Daniella Bernardi

Keyword(s):

Visual Search ◽

Pilot Study ◽

Digital Breast Tomosynthesis ◽

Breast Tomosynthesis ◽

Search Behaviour

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A resampling comparison of CHO's detectability index bias and uncertainty

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2297689 ◽

2018 ◽

Author(s):

Francesc Massanes ◽

Alexandre Ba ◽

François Bochud ◽

Jovan G. Brankov

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A cognitive approach to determine the benefits of pairing radiologists in mammogram reading

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293364 ◽

2018 ◽

Cited By ~ 1

Author(s):

Claudia R. Mello-Thoms ◽

Ziba Gandomkar ◽

Patrick C. Brennan

Keyword(s):

Cognitive Approach

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Analyzing ROC curves using the effective set-size model

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2294529 ◽

2018 ◽

Author(s):

Frank W. Samuelson ◽

Craig K. Abbey ◽

Xin He

Keyword(s):

Roc Curves ◽

Set Size ◽

Size Model

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A database for assessment of effect of lossy compression on digital mammograms

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293828 ◽

2018 ◽

Author(s):

Jiheng Wang ◽

Berkman Sahiner ◽

Nicholas A. Petrick ◽

Aria Pezeshk

Keyword(s):

Lossy Compression

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Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed 'test' dataset and a potential solution

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293818 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alexej Gossmann ◽

Aria Pezeshk ◽

Berkman Sahiner

Keyword(s):

Machine Learning ◽

Test Data ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Data Reuse ◽

Potential Solution ◽

Test Dataset

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Characteristics of the group of radiologists that benefits the most using Breast Screen Reader Assessment Strategy (BREAST)

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ◽

10.1117/12.2293511 ◽

2018 ◽

Author(s):

Aarthi Ganesan ◽

Claudia R. Mello-Thoms ◽

Maram Alakhras ◽

Patrick C. Brennan ◽

Warwick Lee ◽

...

Keyword(s):

Screen Reader ◽

Assessment Strategy ◽

Breast Screen

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Medical Imaging 2018: Image Perception, Observer Performance, and Technology AssessmentLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By SPIE

Front Matter: Volume 10577

Comparing salience detection algorithms in mammograms

Learning to see

Analysis of visual search behaviour from experienced radiologists interpreting digital breast tomosynthesis (DBT) images: a pilot study

A resampling comparison of CHO's detectability index bias and uncertainty

A cognitive approach to determine the benefits of pairing radiologists in mammogram reading

Analyzing ROC curves using the effective set-size model

A database for assessment of effect of lossy compression on digital mammograms

Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed 'test' dataset and a potential solution

Characteristics of the group of radiologists that benefits the most using Breast Screen Reader Assessment Strategy (BREAST)

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Latest Publications