{"id":41277,"date":"2023-12-13T06:16:12","date_gmt":"2023-12-13T12:16:12","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/?post_type=tcia_collection&#038;p=41277"},"modified":"2024-10-15T15:15:44","modified_gmt":"2024-10-15T20:15:44","slug":"breast-lesions-usg","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/breast-lesions-usg\/","title":{"rendered":"BREAST-LESIONS-USG"},"featured_media":16457,"template":"","class_list":["post-41277","tcia_collection","type-tcia_collection","status-publish","has-post-thumbnail"],"cancer_types":["Breast Cancer"],"citations":[41271,47265],"collection_doi":"10.7937\/9WKK-Q141","collection_download_info":"","collection_downloads":[41273,41275],"versions":false,"additional_resources":"The following external resources have been made available by the data submitters.\u00a0 These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.\r\n<ul>\r\n \t<li>Files for simple import of the data into matlab and python variables are available at <a href=\"https:\/\/urldefense.com\/v3\/__https:\/\/github.com\/best-ippt-pan-pl\/BrEaST__;!!LFqOYw!sukPGdUdepUb0p71qoSYEnADea78l-Vr2SRP9UJ10peDcggWMlMOGiwqmE3Ybnd8RZWv--60Uo5_vA1ijw$\">https:\/\/github.com\/best-ippt-pan-pl\/BrEaST<\/a><\/li>\r\n<\/ul>","cancer_locations":["Breast"],"collection_page_accessibility":"Public","publications_related":"No other publications by dataset authors were recommended.","version_change_log_archived":"<h3>Version 1 (Current): Updated 2023\/mm\/dd<\/h3><table><colgroup><col \/><col \/><col \/><\/colgroup><tbody><tr><th>Data Type<\/th><th>Download all or Query\/Filter<\/th><th>License<\/th><\/tr><tr><td>Images and segmentations (PNG zip, 68 MB)<\/td><td><div><p><a href=\"\/wp-content\/uploads\/BrEaST-Lesions_USG-images_and_masks.zip\" download=\"BrEaST-Lesions_USG-images_and_masks.zip\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a><\/p><\/div><\/td><\/tr><tr><td>Clinical data (CSV)<\/td><td><div><p><a href=\"\/wp-content\/uploads\/BrEaST-Lesions-USG-clinical-data-Sep-2023-with-data-dictionary.xlsx\" download=\"BrEaST-Lesions-USG-clinical-data-Sep-2023-with-data-dictionary.xlsx\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage TCIA data. If you have a manuscript you'd like to add please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA's Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"1","collection_title":"A Curated Benchmark Dataset for Ultrasound Based Breast Lesion Analysis","date_updated":"2024-01-08","related_collection":false,"subjects":"256","analysis_results":"","collection_short_title":"Breast-Lesions-USG","data_types":["US","Segmentation"],"version_change_log":"","collection_browse_title":"Breast-Lesions-USG","detailed_description":"","supporting_data":["Software\/Source Code"],"collection_featured_image":{"ID":"16457","post_author":"29","post_date":"2023-10-09 22:07:20","post_date_gmt":"2023-10-10 03:07:20","post_content":"","post_title":"BrEaST-Lesions_WIKI-Image_3","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"breast-lesions_wiki-image_3","to_ping":"","pinged":"","post_modified":"2023-11-29 08:09:57","post_modified_gmt":"2023-11-29 14:09:57","post_content_filtered":"","post_parent":"41277","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/BrEaST-Lesions_WIKI-Image_3.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"16457"},"collection_summary":"<p>This dataset consists of 256 breast ultrasound scans collected from 256 patients and 266 benign and malignant segmented lesions.\u00a0 It includes patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or biopsy result. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image via a freehand annotation and labeled according to BIRADS features. The tumor histopathological classification is stated for patients who underwent a biopsy. Patient-level labels include clinical data such as age, breast tissue composition, signs and symptoms. Image-level freehand annotations identify the tumor and other abnormal areas in the image. The tumor and image are labeled with BIRADS category, 7 BIRADS descriptors, and interpretation of critical findings as presence of breast diseases. Additional labels include the method of verification, tumor classification and histopathological diagnosis.<\/p><p>Since the role of machine learning and theoretical computing towards the development of augmented inference in the field of cancer detection is indisputable, the quality of the data used to develop any explainable augmented inference methods is extremely important. This dataset can be used as an external testing set for assessing a model\u2019s performance and for developing explainable AI or supervised machine learning models for the detection, segmentation and classification of breast abnormalities in ultrasound images.<\/p><p>A detailed description of this dataset can be found here and should be cited along with the citation of the data:<br aria-hidden=\"true\" \/><strong>Paw\u0142owska, A., \u0106wierz-Pie\u0144kowska, A., Domalik, A., Jagu\u015b, D., Kasprzak, P., Matkowski, R., Fura, \u0141., Nowicki, A., &amp; Zolek, N. A Curated benchmark dataset for ultrasound based breast lesion analysis. Sci Data 11, 148 (2024).\u00a0<\/strong><a href=\"https:\/\/doi.org\/10.1038\/s41597-024-02984-z\" target=\"_blank\" rel=\"noopener noreferrer\" data-auth=\"NotApplicable\" data-linkindex=\"0\"><strong>https:\/\/doi.org\/10.1038\/s41597-024-02984-z.<\/strong><\/a><\/p><p>&nbsp;<\/p>","collection_acknowledgements":"<p>We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p><ul><li><p>The preparation of the dataset was supported by National Centre for Research and Development project INFOSTRATEG-I\/0042\/2021<\/p><\/li><\/ul>","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41277","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41277\/revisions"}],"predecessor-version":[{"id":47267,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41277\/revisions\/47267"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/16457"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=41277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}