{"id":5575,"date":"2023-09-04T03:09:38","date_gmt":"2023-09-04T03:09:38","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/c-nmc-2019\/"},"modified":"2023-09-13T11:58:03","modified_gmt":"2023-09-13T11:58:03","slug":"c-nmc-2019","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/c-nmc-2019\/","title":{"rendered":"C-NMC-2019"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Leukemia"],"citations":[4429,4430,2925],"collection_doi":"10.7937\/tcia.2019.dc64i46r","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[4979],"full_export":"<div class=\"contentLayout2\">\n<div class=\"columnLayout two-right-sidebar\" data-layout=\"two-right-sidebar\">\n<div class=\"cell normal\" data-type=\"normal\">\n<div class=\"innerCell\">\n<h2 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-Summary\">Summary<\/h2><div class=\"wiki-content\">Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.<h4 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-Challengeissplitinto3separatephases:\"><strong>Challenge is split into 3 separate phases:<\/strong><\/h4><ul><li><p><strong><span>Train set composition:<\/span><\/strong><\/p><p><span>Total subjects: 73, ALL (cancer): 47, Normal: 26<\/span><\/p><p><span>Total cell images: 10,661, ALL(cancer): 7272, Normal: 3389<\/span> <strong><span><br\/><\/span><\/strong><\/p><\/li><li><p><strong><span>Preliminary test set composition:<\/span><\/strong><\/p><p style=\"text-align: left;\"><span>Total subjects: 28, ALL (cancer): 13, Normal: 15<\/span><\/p><p style=\"text-align: left;\"><span>Total cell images: 1867, ALL(cancer): 1219, Normal: 648<\/span><\/p><\/li><li><p><strong><span>Final test set composition:<\/span><\/strong> <span><br\/><\/span><\/p><p class=\"gmail-Normal1\" style=\"text-align: justify;\"><span>Total subjects: 17, ALL (cancer): 9, Normal: 8<\/span><\/p><p style=\"text-align: left;\"><span>Total cell images: 2586<\/span><\/p><\/li><\/ul><\/div><h4 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-AdditionalPublicationsusingthisdataset:\"><strong>Additional Publications using this dataset:<\/strong><\/h4><p><br\/><\/p><ul><li>Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, &quot;GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images,&quot; Medical Image Analysis, vol. 65, Oct 2020. DOI:\u00a0<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__doi.org_10.1016_j.media.2020.101788&amp;d=DwMGaQ&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=8Cp6b3lxarmeusUZiM4iklR8j0cnPVpMQlwxcUdmg7k&amp;m=Jg1DYoflJFZLyRcI0f0eYIx1CjF5Fi6EE1AoUEp0WxU&amp;s=pKszA6mKSkGqcdE2O8Q6gW6jA23ur9RN8nXVZauH-WE&amp;e=\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.media.2020.101788<\/a>.<\/li><li>Rahul Duggal, Anubha Gupta, Ritu Gupta, and Pramit Mallick, &quot;SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging,&quot; In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention \u2212 MICCAI 2017, MICCAI 2017. Lecture Notes in Computer Science, Part III, LNCS 10435, pp. 435\u2013443. Springer, Cham. DOI:\u00a0<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__doi.org_10.1007_978-2D3-2D319-2D66179-2D7-5F50&amp;d=DwMGaQ&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=8Cp6b3lxarmeusUZiM4iklR8j0cnPVpMQlwxcUdmg7k&amp;m=Jg1DYoflJFZLyRcI0f0eYIx1CjF5Fi6EE1AoUEp0WxU&amp;s=ZqzkCSZyVMEObvm4coXNkoFS1dvmQByJoqCowMjo7u0&amp;e=\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/978-3-319-66179-7_50<\/a>.<\/li><li>Rahul Duggal, Anubha Gupta, Ritu Gupta, Manya Wadhwa, and Chirag Ahuja, \u201cOverlapping Cell Nuclei Segmentation in Microscopic Images UsingDeep Belief Networks,\u201d Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), India, December 2016.<\/li><\/ul><p><br\/><\/p><div class=\"wiki-content\"><div class=\"tab-style-builtin\"><div class=\"localtabs-macro\"><div class=\"aui-tabs horizontal-tabs\" role=\"application\" data-aui-responsive=\"true\"><ul class=\"tabs-menu\"><li class=\"menu-item bv-localtab  active-tab \"><a href=\"#52758223a9c2c0a8b429412880eaa123286ca6f7\"><strong>Data Access<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#5275822326ba7ac67a3a46e482c0f18c4f66554d\"><strong>Detailed Description<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#527582234f616addd24349dc9f47a28a84f56f78\"><strong>Citations &amp; Data Usage Policy<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#52758223a76e826aab7247f8a5c97ab82429b246\"><strong>Versions<\/strong><\/a> <\/li><\/ul><div class=\"tabs-pane  active-pane \" id=\"52758223a9c2c0a8b429412880eaa123286ca6f7\" active=\"true\" name=\"Data Access\" ><h3 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-DataAccess\">Data Access<\/h3><div class=\"table-wrap\"><table class=\"wrapped relative-table confluenceTable\" style=\"width: 76.4668%;\"><colgroup><col style=\"width: 32.4146%;\"\/><col style=\"width: 43.2149%;\"\/><col style=\"width: 24.3619%;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><th class=\"confluenceTh\">License<\/th><\/tr><tr><td class=\"confluenceTd\">Images (BMP, CSV, PDF, 10.44 GB)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/75?passcode=ebf6e585c2b531f41fbcd2b9cfd7b15303eeca80\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/pathdb.cancerimagingarchive.net\/imagesearch?f[0]=collection:c_nmc_2019\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p>(Download and apply the\u00a0<a href=\"https:\/\/www.ibm.com\/aspera\/connect\/\" class=\"external-link\" rel=\"nofollow\">IBM-Aspera-Connect plugin\u00a0<\/a>to your browser to retrieve this faspex package)\u00a0<\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p>Click the Versions tab for more info about data releases.<\/p><\/div><div class=\"tabs-pane \" id=\"5275822326ba7ac67a3a46e482c0f18c4f66554d\" name=\"Detailed Description\" ><h3 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-DetailedDescription\">Detailed Description<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup><col\/><col\/><\/colgroup><tbody><tr><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><div class=\"tablesorter-header-inner\"><p>Image Statistics<\/p><\/div><\/div><\/th><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><div class=\"tablesorter-header-inner\"><p><br\/><\/p><\/div><\/div><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Modalities<\/p><\/td><td class=\"confluenceTd\"><p>Pathology<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Participants<\/p><\/td><td class=\"confluenceTd\"><p>118<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Studies<\/p><\/td><td class=\"confluenceTd\"><p>118<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Images<\/p><\/td><td class=\"confluenceTd\"><p>15,135<\/p><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images Size (GB)<\/td><td colspan=\"1\" class=\"confluenceTd\">10.44<\/td><\/tr><\/tbody><\/table><\/div><p class=\"gmail-Normal1 auto-cursor-target\" style=\"text-align: justify;\">Please see the readme for a more detailed description of the dataset:\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/52758223\/CNMC_readme.pdf?version=1&amp;modificationDate=1559220477330&amp;api=v2\" data-linked-resource-id=\"52762710\" data-linked-resource-version=\"1\" data-linked-resource-type=\"attachment\" data-linked-resource-default-alias=\"CNMC_readme.pdf\" data-nice-type=\"PDF Document\" data-linked-resource-content-type=\"application\/pdf\" data-linked-resource-container-id=\"52758223\" data-linked-resource-container-version=\"34\">CNMC_readme.pdf<\/a><\/p><\/div><div class=\"tabs-pane \" id=\"527582234f616addd24349dc9f47a28a84f56f78\" name=\"Citations BITVOODOO_ANDamp; Data Usage Policy\" ><h3 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-Citations&amp;DataUsagePolicy\">Citations &amp; Data Usage Policy<\/h3><p class=\"auto-cursor-target\">\n<p>\nUsers must abide by the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/c4hF\" class=\"external-link\" rel=\"nofollow\">TCIA Data Usage Policy and Restrictions<\/a>. Attribution should include references to the following citations:\n<\/p><\/p><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Data Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p><span style=\"color: rgb(51,51,51);text-decoration: none;\">&quot;<span style=\"color: rgb(0,0,0);text-decoration: none;\">Gupta, A., &amp; Gupta, R. (2019).<\/span> ALL Challenge dataset of ISBI 2019 [Data set]. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.dc64i46r\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7937\/tcia.2019.dc64i46r<\/a>&quot;<\/span><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Publication Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><ul><li>Shiv Gehlot, Anubha Gupta, and Ritu Gupta, \u201cSDCT-AuxNet\u03b8: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis,\u201d Medical Image Analysis, Elsevier, vol. 61, pp. 1-15, April 2020, DOI:\u00a0<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__doi.org_10.1016_j.media.2020.101661&amp;d=DwMGaQ&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=8Cp6b3lxarmeusUZiM4iklR8j0cnPVpMQlwxcUdmg7k&amp;m=Jg1DYoflJFZLyRcI0f0eYIx1CjF5Fi6EE1AoUEp0WxU&amp;s=UBIF5YWSoglCzxVEnyyKZaS3kH2vKN92v7jraQQbjKU&amp;e=\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.media.2020.101661<\/a>.<\/li><li>Shubham Goswami, Suril Mehta, Dhruv Sahrawat, Anubha Gupta and Ritu Gupta, \u201cHeterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer&quot;, ICLR workshop on Affordable AI in healthcare, 2020. arXiv preprint arXiv:2003.03295.<\/li><\/ul><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">TCIA Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F.\u00a0<strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository<\/strong>, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: <a href=\"https:\/\/doi.org\/10.1007\/s10278-013-9622-7\" class=\"external-link\" rel=\"nofollow\">10.1007\/s10278-013-9622-7<\/a><\/p><\/div><\/div><h3 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-OtherPublicationsUsingThisData\">Other Publications Using This Data<\/h3><p><span>TCIA maintains<\/span> <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\" class=\"external-link\" rel=\"nofollow\"> a list of publications<\/a> <span> which leverage TCIA data. <\/span> If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\" class=\"external-link\" rel=\"nofollow\"> contact the TCIA Helpdesk<\/a>.<\/p><\/div><div class=\"tabs-pane \" id=\"52758223a76e826aab7247f8a5c97ab82429b246\" name=\"Versions\" ><h3 id=\"C_NMC_2019Dataset:ALLChallengedatasetofISBI2019(CNMC2019)-Version1(Current):Updated2019\/03\/26\">Version 1 (Current): Updated 2019\/03\/26<\/h3><div class=\"table-wrap\"><table class=\"wrapped relative-table confluenceTable\" style=\"width: 58.9172%;\"><colgroup><col style=\"width: 44.5451%;\"\/><col style=\"width: 55.3653%;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\"><span>Data Type<\/span><\/th><th class=\"confluenceTh\"><span>Download all or Query\/Filter<\/span><\/th><\/tr><tr><td class=\"confluenceTd\"><span>Images (BMP, CSV, PDF, 10.44 GB)<\/span><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/75?passcode=ebf6e585c2b531f41fbcd2b9cfd7b15303eeca80\" class=\"external-link\" rel=\"nofollow\"><br\/><\/a>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/75?passcode=ebf6e585c2b531f41fbcd2b9cfd7b15303eeca80\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/pathdb.cancerimagingarchive.net\/imagesearch?f[0]=collection:c_nmc_2019\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><\/p><br\/><\/div><\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n<\/div>\n<div class=\"cell aside\" data-type=\"aside\">\n<div class=\"innerCell\">\n<p><br\/><\/p><\/div>\n<\/div>\n<\/div>\n<\/div>","versions":false,"additional_resources":"","cancer_locations":["Blood","Bone"],"collection_page_accessibility":"Public","publications_related":"","version_change_log":"","version_change_log_archived":"","analysis_results":"","collection_status":"Complete","publications_using":"<br\/>\n<ul><li>Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, \"GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images,\" Medical Image Analysis, vol. 65, Oct 2020. DOI:\u00a0<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__doi.org_10.1016_j.media.2020.101788&amp;d=DwMGaQ&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=8Cp6b3lxarmeusUZiM4iklR8j0cnPVpMQlwxcUdmg7k&amp;m=Jg1DYoflJFZLyRcI0f0eYIx1CjF5Fi6EE1AoUEp0WxU&amp;s=pKszA6mKSkGqcdE2O8Q6gW6jA23ur9RN8nXVZauH-WE&amp;e=\">https:\/\/doi.org\/10.1016\/j.media.2020.101788<\/a>.<\/li><li>Rahul Duggal, Anubha Gupta, Ritu Gupta, and Pramit Mallick, \"SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging,\" In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention \u2212 MICCAI 2017, MICCAI 2017. Lecture Notes in Computer Science, Part III, LNCS 10435, pp. 435\u2013443. Springer, Cham. DOI:\u00a0<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__doi.org_10.1007_978-2D3-2D319-2D66179-2D7-5F50&amp;d=DwMGaQ&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=8Cp6b3lxarmeusUZiM4iklR8j0cnPVpMQlwxcUdmg7k&amp;m=Jg1DYoflJFZLyRcI0f0eYIx1CjF5Fi6EE1AoUEp0WxU&amp;s=ZqzkCSZyVMEObvm4coXNkoFS1dvmQByJoqCowMjo7u0&amp;e=\">https:\/\/doi.org\/10.1007\/978-3-319-66179-7_50<\/a>.<\/li><li>Rahul Duggal, Anubha Gupta, Ritu Gupta, Manya Wadhwa, and Chirag Ahuja, \u201cOverlapping Cell Nuclei Segmentation in Microscopic Images UsingDeep Belief Networks,\u201d Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), India, December 2016.<\/li><\/ul>\n<br\/>","species":["Human"],"collection_title":"C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019","detailed_description":"Please see the readme for a more detailed description of the dataset:\u00a0<a data-linked-resource-container-id=\"52758223\" data-linked-resource-container-version=\"34\" data-linked-resource-content-type=\"application\/pdf\" data-linked-resource-default-alias=\"CNMC_readme.pdf\" data-linked-resource-id=\"52762710\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"PDF Document\" download=\"\" href=\"\/wp-content\/uploads\/CNMC_readme.pdf\" target=\"_blank\">CNMC_readme.pdf<\/a>","related_analysis_results":false,"subjects":"118","collection_short_title":"C-NMC 2019","data_types":["Pathology"],"date_updated":"2023-09-13","collection_browse_title":"","supporting_data":false,"collection_featured_image":false,"collection_summary":"Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.<h4><strong>Challenge is split into 3 separate phases:<\/strong><\/h4><ul><li><p><strong>Train set composition:<\/strong><\/p><p>Total subjects: 73, ALL (cancer): 47, Normal: 26<\/p><p>Total cell images: 10,661, ALL(cancer): 7272, Normal: 3389 <strong><br\/><\/strong><\/p><\/li><li><p><strong>Preliminary test set composition:<\/strong><\/p><p>Total subjects: 28, ALL (cancer): 13, Normal: 15<\/p><p>Total cell images: 1867, ALL(cancer): 1219, Normal: 648<\/p><\/li><li><p><strong>Final test set composition:<\/strong> <br\/><\/p><p>Total subjects: 17, ALL (cancer): 9, Normal: 8<\/p><p>Total cell images: 2586<\/p><\/li><\/ul>","collection_acknowledgements":"","collection_funding":"","hide_from_browse_table":[],"_links":{"self":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections\/5575"}],"collection":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections"}],"about":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/types\/tcia_collection"}],"wp:attachment":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/media?parent=5575"}],"wp:term":[{"taxonomy":"tcia_citation_tax","embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/v1\/citation-tax?post=5575"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}