A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
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{"title"=>"A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data", "type"=>"journal", "authors"=>[{"first_name"=>"Sandra", "last_name"=>"Ortega-Martorell", "scopus_author_id"=>"36090096200"}, {"first_name"=>"Héctor", "last_name"=>"Ruiz", "scopus_author_id"=>"55337826800"}, {"first_name"=>"Alfredo", "last_name"=>"Vellido", "scopus_author_id"=>"6602481203"}, {"first_name"=>"Iván", "last_name"=>"Olier", "scopus_author_id"=>"8914762100"}, {"first_name"=>"Enrique", "last_name"=>"Romero", "scopus_author_id"=>"56359064800"}, {"first_name"=>"Margarida", "last_name"=>"Julià-Sapé", "scopus_author_id"=>"23392766000"}, {"first_name"=>"José D.", "last_name"=>"Martín", "scopus_author_id"=>"7501551869"}, {"first_name"=>"Ian H.", "last_name"=>"Jarman", "scopus_author_id"=>"23389018100"}, {"first_name"=>"Carles", "last_name"=>"Arús", "scopus_author_id"=>"7003464896"}, {"first_name"=>"Paulo J.G.", "last_name"=>"Lisboa", "scopus_author_id"=>"35583079900"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84893482172", "doi"=>"10.1371/journal.pone.0083773", "pui"=>"372292907", "pmid"=>"24376744", "scopus"=>"2-s2.0-84893482172", "issn"=>"19326203"}, "id"=>"5609fcaa-b1ce-36ee-8150-afac53b0e872", "abstract"=>"BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.\\n\\nCONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing.", "link"=>"http://www.mendeley.com/research/novel-semisupervised-methodology-extracting-tumor-typespecific-mrs-sources-human-brain-data", "reader_count"=>24, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Master"=>1, "Student > Bachelor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Master"=>1, "Student > Bachelor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>8, "Agricultural and Biological Sciences"=>4, "Psychology"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>8}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "Brazil"=>1, "United Kingdom"=>1, "Italy"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1326918"], "description"=>"<p>Summary of the labeling accuracy obtained for the training and test set when three sources were calculated in a fully unsupervised way (Convex-NMF), for two discrimination problems at STE and LTE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "unsupervised"], "article_id"=>885344, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t006", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_with_3_sources_unsupervised_Convex_NMF_/885344", "title"=>"Labeling accuracy results obtained with 3 sources, unsupervised (Convex-NMF).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326916"], "description"=>"<p>Summary of the labeling accuracy obtained for the test set, for all the discrimination problems at LTE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification. Highest total accuracy and lowest BER underlined as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-t002\" target=\"_blank\">table 2</a>.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>885342, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_for_the_test_set_at_LTE_/885342", "title"=>"Labeling accuracy results obtained for the test set at LTE.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326912"], "description"=>"<p>Three sources extracted in unsupervised mode, using Convex-NMF, for the aggressive tumors group (GL + ME), using the training data at both STE and LTE. Axes labels as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-g003\" target=\"_blank\">figure 3</a>.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "sources", "extracted", "unsupervised", "methods", "ag"], "article_id"=>885338, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.g005", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Three_sources_extracted_through_unsupervised_methods_for_the_AG_group_/885338", "title"=>"Three sources extracted through unsupervised methods for the AG group.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326907"], "description"=>"<p>General representation of the unsupervised and semi-supervised approaches analyzed in this study for extracting specific MRS sources in human brain tumors.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "analytical", "approaches", "investigated"], "article_id"=>885333, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_General_representation_of_the_analytical_approaches_investigated_in_this_study_/885333", "title"=>"General representation of the analytical approaches investigated in this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326921"], "description"=>"<p>Summary of the labeling accuracy obtained for the training set, for all the discrimination problems at STE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification. The highest total accuracy and the lowest BER for each classification problem are underlined.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>885347, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_for_the_training_set_at_STE_/885347", "title"=>"Labeling accuracy results obtained for the training set at STE.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326920"], "description"=>"<p>Table cells should be read as the correlations between the sources and the average spectra (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-g003\" target=\"_blank\">figures 3</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-g004\" target=\"_blank\">4</a>) of the different tumor types. The results of the best performing method for each classification problem are underlined. The latter is measured as the highest average value between the two correlations. When this highest average value is obtained by more than one method, all their corresponding results are underlined.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "sources"], "article_id"=>885346, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_between_the_sources_and_the_average_spectra_/885346", "title"=>"Correlations between the sources and the average spectra.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326902"], "description"=>"<p>Four STE cases selected from the INTERPRET dataset that illustrate the heterogeneity of the GL group, with I0145 showing a necrotic pattern, and I1098 showing an actively proliferating behavior, similar to that of I1041, its low-grade counterpart. These selected cases also illustrate the similarities of I0145 and I0211, which are highly correlated to each other, but are tumor types with different histopathological origins.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "cases"], "article_id"=>885328, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_cases_from_the_INTERPRET_dataset_/885328", "title"=>"Selected cases from the INTERPRET dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326915"], "description"=>"<p>Summary of the labeling accuracy obtained for the test set, for all the discrimination problems at STE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification. Highest total accuracy and lowest BER underlined as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-t002\" target=\"_blank\">table 2</a>.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>885341, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_for_the_test_set_at_STE_/885341", "title"=>"Labeling accuracy results obtained for the test set at STE.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326914"], "description"=>"<p>Representation of the three sources to the two tumor types (GL and ME) involved. They include the percentage of cases mainly represented by each source (by tumor type), and the number of cases from the total, in parentheses. Sources were extracted in an unsupervised mode using Convex-NMF for the aggressive tumors group (GL + ME), using the training data at both STE and LTE.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "sources", "extracted", "unsupervised"], "article_id"=>885340, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t008", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representation_of_the_three_sources_extracted_in_unsupervised_mode_for_GL_ME_/885340", "title"=>"Representation of the three sources extracted in unsupervised mode for GL+ME.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326911"], "description"=>"<p>Sources extracted for all the classification problems using the training data at LTE, for two of the approaches: Convex-NMF (unsupervised), and IMA + Convex-NMF (semi-supervised). The blue spectra indicate the mean of the classes involved. Axes labels and representation as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-g003\" target=\"_blank\">figure 3</a>.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "extracted", "unsupervised", "semi-supervised"], "article_id"=>885337, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_extracted_through_unsupervised_and_semi_supervised_methods_at_LTE_/885337", "title"=>"Sources extracted through unsupervised and semi-supervised methods, at LTE.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326908"], "description"=>"<p>Sources extracted for all the classification problems using the training data at STE, for two of the approaches: Convex-NMF (unsupervised), and IMA + Convex-NMF (semi-supervised). The blue spectra indicate the mean of the classes involved. Horizontal axis, for all plots: frequency in ppm scale. Vertical axis, for all plots: UL2 normalized intensity. The range of the vertical scales is fixed for each experiment and is the same for comparative purposes.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "extracted", "unsupervised", "semi-supervised"], "article_id"=>885334, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.g003", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_extracted_through_unsupervised_and_semi_supervised_methods_at_STE_/885334", "title"=>"Sources extracted through unsupervised and semi-supervised methods, at STE.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326922"], "description"=>"<p>Summary of the labeling accuracy obtained for the training set, for all the discrimination problems at LTE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification. Highest total accuracy and lowest BER underlined as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083773#pone-0083773-t002\" target=\"_blank\">table 2</a>.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>885348, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_for_the_training_set_at_LTE_/885348", "title"=>"Labeling accuracy results obtained for the training set at LTE.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1326919"], "description"=>"<p>Summary of the labeling accuracy obtained for the training and test set when three sources were calculated in a fully unsupervised way, and a semi-supervised way (IMA+Convex-NMF), for the discrimination problem A2 <i>vs.</i> AG (GL+ME) at STE and LTE. They include the accuracy (total and by tumor type); the number of correctly labeled samples from the total, in parentheses; and BER of the classification.</p>", "links"=>[], "tags"=>["Computing methods", "Mathematical computing", "signal processing", "Applied mathematics", "oncology", "Cancers and neoplasms", "Neurological tumors", "Basic cancer research", "Cancer detection and diagnosis", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "semi-supervised", "a2", "ag"], "article_id"=>885345, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering"], "users"=>["Sandra Ortega-Martorell", "Héctor Ruiz", "Alfredo Vellido", "Iván Olier", "Enrique Romero", "Margarida Julià-Sapé", "José D. Martín", "Ian H. Jarman", "Carles Arús", "Paulo J. G. Lisboa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083773.t007", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Labeling_accuracy_results_obtained_with_3_sources_semi_supervised_and_unsupervised_for_A2_vs_AG_GL_ME_/885345", "title"=>"Labeling accuracy results obtained with 3 sources, semi-supervised and unsupervised, for A2 <i>vs.</i> AG (GL+ME).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-23 03:36:18"}

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Relative Metric

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