Severity: Warning
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Filename: drivers/Session_files_driver.php
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Backtrace:
File: /var/www/html/index.php
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Function: require_once
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
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Function: require_once
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Message: strpos(): Passing null to parameter #1 ($haystack) of type string is deprecated
Filename: models/Detail_model.php
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File: /var/www/html/application/models/Detail_model.php
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Function: strpos
File: /var/www/html/application/controllers/Detail.php
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Function: insertAPISummary
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Function: require_once
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Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: str_replace
File: /var/www/html/application/controllers/Detail.php
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Function: formatAIDetailSummary
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "choices"
Filename: controllers/Detail.php
Line Number: 256
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "usage"
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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Line: 257
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 258
Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: file_get_contents
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: getPubMedXML
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Function: pubMedGetRelatedKeyword
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Function: pubMedGetRelatedKeyword
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Function: require_once
Purpose: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts.
Materials And Methods: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra-high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs.
Results: A quantitative evaluation of distributional alignment shows 60%-78% mutual-information score between the real and synthetic image distributions, and 80%-91% overlap in their support, which are strong indications against mode collapse. It also reveals shape misalignment as the main difference between the two distributions. Obvious artifacts were found by an untrained observer in 13.6% and 6.4% of the synthetic mediolateral oblique and craniocaudal images, respectively. A reader study demonstrated that real and synthetic images are perceptually inseparable by the majority of participants, even by trained breast radiologists. Only one out of the 117 participants was able to reliably distinguish real from synthetic images, and this study discusses the cues they used to do so.
Conclusion: On the basis of these findings, it appears possible to generate realistic synthetic full-field digital mammograms by using a progressive GAN architecture up to a resolution of 1280 × 1024 pixels.© RSNA, 2020.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043361 | PMC |
http://dx.doi.org/10.1148/ryai.2020190181 | DOI Listing |
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