Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images
Publication Date
November 18, 2015
Journal
PLOS ONE
Authors
Richard M. Levenson, Elizabeth A. Krupinski, Victor M. Navarro & Edward A. Wasserman
Volume
10
Issue
11
Pages
e0141357
DOI
https://dx.plos.org/10.1371/journal.pone.0141357
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0141357
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26581091
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4651348
Europe PMC
http://europepmc.org/abstract/MED/26581091
Web of Science
000365154600014
Scopus
84954447479
Mendeley
http://www.mendeley.com/research/pigeons-columba-livia-trainable-observers-pathology-radiology-breast-cancer-images
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Mendeley | Further Information

{"title"=>"Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images", "type"=>"journal", "authors"=>[{"first_name"=>"Richard M.", "last_name"=>"Levenson", "scopus_author_id"=>"36876359100"}, {"first_name"=>"Elizabeth A.", "last_name"=>"Krupinski", "scopus_author_id"=>"26643320200"}, {"first_name"=>"Victor M.", "last_name"=>"Navarro", "scopus_author_id"=>"57102811300"}, {"first_name"=>"Edward A.", "last_name"=>"Wasserman", "scopus_author_id"=>"35549368300"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84954447479", "pmid"=>"26581091", "sgr"=>"84954447479", "issn"=>"19326203", "pui"=>"608121435", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0141357"}, "id"=>"b124e651-83bc-3e41-bdb8-8a2cb6c8679b", "abstract"=>"Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)-which share many visual system properties with humans-can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds' histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task-namely, classification of suspicious mammographic densities (masses)-the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds' successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.", "link"=>"http://www.mendeley.com/research/pigeons-columba-livia-trainable-observers-pathology-radiology-breast-cancer-images", "reader_count"=>180, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>11, "Librarian"=>3, "Student > Doctoral Student"=>10, "Researcher"=>32, "Student > Ph. D. 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Student"=>45, "Student > Postgraduate"=>11, "Other"=>13, "Student > Master"=>23, "Student > Bachelor"=>16, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>3, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Agricultural and Biological Sciences"=>28, "Arts and Humanities"=>3, "Philosophy"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Chemistry"=>1, "Computer Science"=>12, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>3, "Economics, Econometrics and Finance"=>1, "Engineering"=>14, "Biochemistry, Genetics and Molecular Biology"=>13, "Nursing and Health Professions"=>1, "Materials Science"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>50, "Neuroscience"=>4, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>12, "Psychology"=>17, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>50}, "Social Sciences"=>{"Social Sciences"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>12}, "Psychology"=>{"Psychology"=>17}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>11}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>14}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>12}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Philosophy"=>{"Philosophy"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Canada"=>6, "United States"=>6, "Luxembourg"=>1, "Japan"=>3, "Finland"=>1, "Brazil"=>1, "Poland"=>1, "Italy"=>2, "United Kingdom"=>5, "Switzerland"=>1, "Germany"=>4, "Ethiopia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4323439"], "description"=>"<p>A “flock-sourcing” score was calculated by summating the responses of individual birds as described in the text. Pooling the birds’ decisions led to significantly better discrimination than that achieved by individual pigeons. The dotted line represents no discrimination between benign and malignant exemplars.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608256, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g009"], "stats"=>{"downloads"=>12, "page_views"=>270, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flock_sourcing_/1608256", "title"=>"Flock sourcing.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323448"], "description"=>"<p>When correct/incorrect responses were nondifferentially reinforced (gray bars), pigeons’ accuracy was affected proportionally to the compression level of the images shown. However, pigeons were capable of achieving high levels of accuracy with compressed images if feedback for correct/incorrect responses was given (white bars).</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608257, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g010"], "stats"=>{"downloads"=>5, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_JPEG_image_compression_/1608257", "title"=>"Effect of JPEG image compression.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323454"], "description"=>"<p>A) Training quickly led to high levels of accuracy. B) The pigeons were able to generalize to novel images, but their performance on this task was not as good as their generalization to novel histology images (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141357#pone.0141357.g007\" target=\"_blank\">Fig 7</a>), although still above chance levels of responding. The trend observed on Day 1 of testing (left) continued throughout the remainder of testing (right).</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608258, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g011"], "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_training_and_testing_with_mammograms_with_or_without_calcifications_/1608258", "title"=>"Results of training and testing with mammograms with or without calcifications.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323460"], "description"=>"<p>A) Pigeons required long training to discriminate between mammograms with masses, and even then, individual differences were pronounced. B) Regardless of their performance in the training phase, all of the pigeons failed to transfer their performance to novel exemplars, suggesting that their performance was based on rote memorization.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608259, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g012"], "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_training_and_testing_with_mammograms_containing_masses_/1608259", "title"=>"Results of training and testing with mammograms containing masses.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323463"], "description"=>"<p>During Experiment 1, some exemplars from a given category looked like exemplars from the other category causing the birds to incorrectly categorize them.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608267, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g013"], "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conflictive_histology_exemplars_/1608267", "title"=>"Conflictive histology exemplars.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323370", "https://ndownloader.figshare.com/files/4323376", "https://ndownloader.figshare.com/files/4323379"], "description"=>"<div><p>Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (<i>Columba livia</i>)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.</p></div>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608270, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.s001", "https://dx.doi.org/10.1371/journal.pone.0141357.s002", "https://dx.doi.org/10.1371/journal.pone.0141357.s003"], "stats"=>{"downloads"=>7, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Pigeons_Columba_livia_as_Trainable_Observers_of_Pathology_and_Radiology_Breast_Cancer_Images/1608270", "title"=>"Pigeons (<i>Columba livia</i>) as Trainable Observers of Pathology and Radiology Breast Cancer Images", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323382"], "description"=>"<p>The operant conditioning chamber was equipped with a food pellet dispenser, and a touch-sensitive screen upon which the medical image (center) and choice buttons (blue and yellow rectangles) were presented.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608241, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g001"], "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_pigeons_8217_training_environment_/1608241", "title"=>"The pigeons’ training environment.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323388"], "description"=>"<p>Pigeons were initially trained and tested with samples at 4× magnification (top row), and then were subsequently transitioned to samples at 10× magnification (center row) and 20× magnification (bottom row).</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608246, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g002"], "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_benign_left_and_malignant_right_breast_specimens_stained_with_hematoxylin_and_eosin_at_different_magnifications_/1608246", "title"=>"Examples of benign (left) and malignant (right) breast specimens stained with hematoxylin and eosin, at different magnifications.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323397"], "description"=>"<p>The original images at 10× magnification were converted to grayscale, colored with a single hue, and had their overall brightness and contrast equalized as closely as possible. Additionally, the images were reduced to 7% (1:15, middle row) or 4% (1:27, bottom row) of their original size, to create the compressed sets.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608248, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g003"], "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Monochrome_images_with_equated_hue_and_brightness_at_different_levels_of_compression_/1608248", "title"=>"Monochrome images with equated hue and brightness, at different levels of compression.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323403"], "description"=>"<p>Yellow circles denote where microcalcifications are located.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608250, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g004"], "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mammograms_with_the_absence_left_and_with_presence_right_of_microcalcifications_/1608250", "title"=>"Mammograms with the absence (left) and with presence (right) of microcalcifications.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323409"], "description"=>"<p>Subsequent biopsy established histopathology ground-truth.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608252, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g005"], "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_benign_left_and_malignant_right_masses_in_mammograms_/1608252", "title"=>"Examples of benign (left) and malignant (right) masses in mammograms.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323421"], "description"=>"<p>A) When first trained with 4× magnification images the birds performed at chance levels of accuracy, but quickly learned to discriminate. Subsequently, when the birds were exposed to higher magnifications samples, their performance commenced at accuracies above chance (but below their final performance at lower magnification they had previously been exposed to), and improved further with training. B) Introducing rotated versions of the training stimuli did not significantly affect performance at any of the magnifications.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608253, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g006"], "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_training_with_breast_histopathology_samples_at_different_magnifications_and_rotations_/1608253", "title"=>"Results of training with breast histopathology samples at different magnifications and rotations.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323430"], "description"=>"<p>After training with differential reinforcement, the birds successfully classified previously unseen breast tissue images in the testing sets, at all magnifications, with no statistically significant decrease in accuracy compared to training-set performance.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608254, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g007"], "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generalization_from_training_to_test_image_sets_/1608254", "title"=>"Generalization from training to test image sets.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/4323436"], "description"=>"<p>A) The pigeons were able to learn discrimination without the benefit of hue and brightness cues. B) However, the lack of these cues diminished the birds’ ability to generalize to new images; compared to an equivalent test of full-color exemplars (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141357#pone.0141357.g007\" target=\"_blank\">Fig 7</a>), the pigeons performed significantly more poorly, although still well above chance levels.</p>", "links"=>[], "tags"=>["Key insights", "animal models", "mammogram images", "image compression", "imaging hardware", "image memorization", "breast histopathology", "mammographic densities", "food reinforcement", "Radiology Breast Cancer Images Pathologists", "novel examples", "novel image sets", "image processing", "system properties", "image perception", "image features", "Columba livia", "image analysis tools", "Trainable Observers", "performance assessment"], "article_id"=>1608255, "categories"=>["Biochemistry", "Space Science", "Medicine", "Cell Biology", "Neuroscience", "Sociology", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Richard M. Levenson", "Elizabeth A. Krupinski", "Victor M. Navarro", "Edward A. Wasserman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141357.g008"], "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Training_and_testing_with_hue_and_brightness_normalized_breast_histology_images_/1608255", "title"=>"Training and testing with hue- and brightness-normalized breast histology images.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-18 10:41:06"}

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  • {"unique-ip"=>"165", "full-text"=>"219", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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