{"id":41599,"date":"2023-11-20T01:39:24","date_gmt":"2023-11-20T07:39:24","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/breast-cancer-screening-dbt\/"},"modified":"2024-10-15T14:40:51","modified_gmt":"2024-10-15T19:40:51","slug":"breast-cancer-screening-dbt","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/breast-cancer-screening-dbt\/","title":{"rendered":"BREAST-CANCER-SCREENING-DBT"},"featured_media":0,"template":"","class_list":["post-41599","tcia_collection","type-tcia_collection","status-publish"],"cancer_types":["Breast Cancer"],"citations":[41537,41539,9225],"collection_doi":"10.7937\/E4WT-CD02","collection_download_info":"","collection_downloads":[41541,41543,41545,41547,41549,41551,47767,47787,41553,41555,47825,47835,47655],"versions":[41593,41595,41597],"additional_resources":"The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=breast_cancer_screening_dbt\">Imaging Data Commons (IDC)<\/a>\u00a0(Imaging Data)<\/li>\r\n<\/ul>\r\nThe 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>Source code: <a href=\"https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\">https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data<\/a><\/li>\r\n \t<li><a href=\"https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/\">https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex\">https:\/\/www.aapm.org\/GrandChallenge\/DBTex<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex2\">https:\/\/www.aapm.org\/GrandChallenge\/DBTex2<\/a><\/li>\r\n<\/ul>","cancer_locations":["Breast"],"collection_page_accessibility":"Public","publications_related":".","version_change_log_archived":"<h3>Version 4 (Current): Updated 2021\/06\/08<\/h3><table><colgroup><col \/><col \/><\/colgroup><tbody><tr><th>Data Type<\/th><th>Download all or Query\/Filter<\/th><\/tr><tr><td><p>Training set - Images (DICOM, 1321 GB)<\/p><\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BSC-DBT-Train-manifest.tcia\" download=\"BSC-DBT-Train-manifest.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-train-v2.csv\" download=\"BCS-DBT-file-paths-train-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups)\u00a0(csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-labels-train-v2.csv\" download=\"BCS-DBT-labels-train-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set Phase 2 - Boxes indicating lesion locations, identical to Phase 1\u00a0(csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-boxes-train-v2.csv\" download=\"BCS-DBT-boxes-train-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Validation set - Images (DICOM, 79 GB)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BSC-DBT-Validation-manifest.tcia\" download=\"BSC-DBT-Validation-manifest.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;PatientCriteria=DBT-P00001,DBT-P00002,DBT-P00018,DBT-P00047,DBT-P00069,DBT-P00082,DBT-P00084,DBT-P00114,DBT-P00133,DBT-P00160,DBT-P00165,DBT-P00174,DBT-P00176,DBT-P00187,DBT-P00213,DBT-P00234,DBT-P00235,DBT-P00238,DBT-P00271,DBT-P00273,DBT-P00283,DBT-P00351,DBT-P00385,DBT-P00417,DBT-P00431,DBT-P00450,DBT-P00547,DBT-P00549,DBT-P00558,DBT-P00577,DBT-P00585,DBT-P00643,DBT-P00651,DBT-P00655,DBT-P00697,DBT-P00705,DBT-P00714,DBT-P00718,DBT-P00789,DBT-P00802,DBT-P00803,DBT-P00839,DBT-P00843,DBT-P00844,DBT-P00864,DBT-P00866,DBT-P00871,DBT-P00878,DBT-P00902,DBT-P00908,DBT-P00925,DBT-P00945,DBT-P01020,DBT-P01043,DBT-P01047,DBT-P01083,DBT-P01105,DBT-P01117,DBT-P01150,DBT-P01152,DBT-P01153,DBT-P01173,DBT-P01177,DBT-P01206,DBT-P01207,DBT-P01212,DBT-P01226,DBT-P01239,DBT-P01249,DBT-P01293,DBT-P01296,DBT-P01305,DBT-P01334,DBT-P01353,DBT-P01356,DBT-P01374,DBT-P01490,DBT-P01496,DBT-P01536,DBT-P01538,DBT-P01555,DBT-P01562,DBT-P01564,DBT-P01578,DBT-P01604,DBT-P01610,DBT-P01630,DBT-P01634,DBT-P01635,DBT-P01642,DBT-P01700,DBT-P01705,DBT-P01746,DBT-P01772,DBT-P01778,DBT-P01791,DBT-P01792,DBT-P01799,DBT-P01807,DBT-P01849,DBT-P01851,DBT-P01916,DBT-P01932,DBT-P01938,DBT-P01941,DBT-P01948,DBT-P01957,DBT-P01967,DBT-P02012,DBT-P02018,DBT-P02042,DBT-P02072,DBT-P02085,DBT-P02111,DBT-P02113,DBT-P02117,DBT-P02160,DBT-P02170,DBT-P02183,DBT-P02185,DBT-P02189,DBT-P02204,DBT-P02214,DBT-P02226,DBT-P02242,DBT-P02248,DBT-P02253,DBT-P02284,DBT-P02294,DBT-P02296,DBT-P02301,DBT-P02314,DBT-P02322,DBT-P02326,DBT-P02331,DBT-P02341,DBT-P02360,DBT-P02381,DBT-P02394,DBT-P02469,DBT-P02498,DBT-P02501,DBT-P02516,DBT-P02526,DBT-P02528,DBT-P02545,DBT-P02558,DBT-P02560,DBT-P02574,DBT-P02598,DBT-P02648,DBT-P02669,DBT-P02686,DBT-P02732,DBT-P02754,DBT-P02766,DBT-P02776,DBT-P02816,DBT-P02864,DBT-P02898,DBT-P02946,DBT-P02959,DBT-P02989,DBT-P03002,DBT-P03027,DBT-P03038,DBT-P03056,DBT-P03092,DBT-P03097,DBT-P03110,DBT-P03113,DBT-P03129,DBT-P03166,DBT-P03188,DBT-P03190,DBT-P03215,DBT-P03240,DBT-P03263,DBT-P03317,DBT-P03340,DBT-P03343,DBT-P03346,DBT-P03360,DBT-P03370,DBT-P03377,DBT-P03390,DBT-P03403,DBT-P03448,DBT-P03464,DBT-P03470,DBT-P03483,DBT-P03501,DBT-P03543,DBT-P03574,DBT-P03576,DBT-P03582,DBT-P03592,DBT-P03598,DBT-P03605,DBT-P03621,DBT-P03628,DBT-P03651,DBT-P03689,DBT-P03694,DBT-P03710,DBT-P03725,DBT-P03726,DBT-P03728,DBT-P03831,DBT-P03898,DBT-P03901,DBT-P03932,DBT-P03936,DBT-P03937,DBT-P03948,DBT-P03968,DBT-P03979,DBT-P04008,DBT-P04017,DBT-P04031,DBT-P04033,DBT-P04037,DBT-P04045,DBT-P04081,DBT-P04084,DBT-P04087,DBT-P04097,DBT-P04105,DBT-P04106,DBT-P04139,DBT-P04158,DBT-P04163,DBT-P04171,DBT-P04174,DBT-P04208,DBT-P04210,DBT-P04228,DBT-P04247,DBT-P04257,DBT-P04293,DBT-P04295,DBT-P04299,DBT-P04310,DBT-P04317,DBT-P04344,DBT-P04346,DBT-P04368,DBT-P04380,DBT-P04396,DBT-P04446,DBT-P04452,DBT-P04455,DBT-P04497,DBT-P04515,DBT-P04517,DBT-P04541,DBT-P04577,DBT-P04586,DBT-P04596,DBT-P04622,DBT-P04642,DBT-P04677,DBT-P04684,DBT-P04693,DBT-P04695,DBT-P04697,DBT-P04744,DBT-P04803,DBT-P04808,DBT-P04837,DBT-P04857,DBT-P04874,DBT-P04884,DBT-P04911,DBT-P04963,DBT-P05000,DBT-P05005,DBT-P05018,DBT-P05025,DBT-P05060&amp;CollectionCriteria=Breast-Cancer-Screening-DBT\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Validation set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-validation-v2.csv\" download=\"BCS-DBT-file-paths-validation-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Test set - Images (DICOM, 126 GB)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BSC-DBT-Test-manifest.tcia\" download=\"BSC-DBT-Test-manifest.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;PatientCriteria=DBT-P00004,DBT-P00021,DBT-P00036,DBT-P00056,DBT-P00073,DBT-P00075,DBT-P00087,DBT-P00092,DBT-P00095,DBT-P00118,DBT-P00129,DBT-P00144,DBT-P00192,DBT-P00208,DBT-P00211,DBT-P00221,DBT-P00223,DBT-P00239,DBT-P00276,DBT-P00277,DBT-P00281,DBT-P00284,DBT-P00311,DBT-P00312,DBT-P00317,DBT-P00318,DBT-P00332,DBT-P00339,DBT-P00343,DBT-P00345,DBT-P00350,DBT-P00372,DBT-P00386,DBT-P00387,DBT-P00392,DBT-P00455,DBT-P00471,DBT-P00472,DBT-P00480,DBT-P00487,DBT-P00493,DBT-P00496,DBT-P00522,DBT-P00556,DBT-P00561,DBT-P00573,DBT-P00584,DBT-P00590,DBT-P00601,DBT-P00615,DBT-P00619,DBT-P00620,DBT-P00623,DBT-P00629,DBT-P00635,DBT-P00647,DBT-P00659,DBT-P00675,DBT-P00690,DBT-P00694,DBT-P00712,DBT-P00715,DBT-P00721,DBT-P00724,DBT-P00735,DBT-P00741,DBT-P00756,DBT-P00758,DBT-P00770,DBT-P00772,DBT-P00794,DBT-P00798,DBT-P00801,DBT-P00817,DBT-P00838,DBT-P00857,DBT-P00872,DBT-P00882,DBT-P00915,DBT-P00918,DBT-P00935,DBT-P00936,DBT-P00937,DBT-P00977,DBT-P00980,DBT-P00984,DBT-P00989,DBT-P01010,DBT-P01015,DBT-P01022,DBT-P01023,DBT-P01024,DBT-P01059,DBT-P01060,DBT-P01068,DBT-P01129,DBT-P01137,DBT-P01140,DBT-P01158,DBT-P01183,DBT-P01184,DBT-P01210,DBT-P01219,DBT-P01237,DBT-P01247,DBT-P01256,DBT-P01263,DBT-P01275,DBT-P01277,DBT-P01280,DBT-P01283,DBT-P01291,DBT-P01292,DBT-P01298,DBT-P01340,DBT-P01342,DBT-P01367,DBT-P01371,DBT-P01372,DBT-P01373,DBT-P01409,DBT-P01423,DBT-P01443,DBT-P01448,DBT-P01450,DBT-P01451,DBT-P01487,DBT-P01497,DBT-P01510,DBT-P01531,DBT-P01534,DBT-P01542,DBT-P01547,DBT-P01552,DBT-P01563,DBT-P01577,DBT-P01592,DBT-P01593,DBT-P01612,DBT-P01621,DBT-P01622,DBT-P01623,DBT-P01629,DBT-P01652,DBT-P01655,DBT-P01670,DBT-P01702,DBT-P01719,DBT-P01725,DBT-P01726,DBT-P01739,DBT-P01755,DBT-P01782,DBT-P01797,DBT-P01803,DBT-P01814,DBT-P01837,DBT-P01847,DBT-P01862,DBT-P01865,DBT-P01880,DBT-P01888,DBT-P01892,DBT-P01896,DBT-P01898,DBT-P01899,DBT-P01912,DBT-P01918,DBT-P01919,DBT-P01934,DBT-P01958,DBT-P01961,DBT-P01963,DBT-P01983,DBT-P01990,DBT-P01991,DBT-P02006,DBT-P02009,DBT-P02028,DBT-P02055,DBT-P02069,DBT-P02094,DBT-P02104,DBT-P02110,DBT-P02112,DBT-P02123,DBT-P02131,DBT-P02139,DBT-P02152,DBT-P02155,DBT-P02157,DBT-P02164,DBT-P02172,DBT-P02180,DBT-P02187,DBT-P02190,DBT-P02199,DBT-P02232,DBT-P02235,DBT-P02281,DBT-P02285,DBT-P02293,DBT-P02306,DBT-P02308,DBT-P02312,DBT-P02313,DBT-P02325,DBT-P02347,DBT-P02420,DBT-P02460,DBT-P02477,DBT-P02487,DBT-P02496,DBT-P02511,DBT-P02512,DBT-P02514,DBT-P02542,DBT-P02559,DBT-P02572,DBT-P02578,DBT-P02604,DBT-P02609,DBT-P02651,DBT-P02657,DBT-P02665,DBT-P02668,DBT-P02678,DBT-P02685,DBT-P02711,DBT-P02714,DBT-P02719,DBT-P02725,DBT-P02733,DBT-P02742,DBT-P02744,DBT-P02748,DBT-P02753,DBT-P02774,DBT-P02794,DBT-P02797,DBT-P02804,DBT-P02817,DBT-P02825,DBT-P02826,DBT-P02834,DBT-P02861,DBT-P02875,DBT-P02901,DBT-P02906,DBT-P02910,DBT-P02921,DBT-P02933,DBT-P02937,DBT-P02941,DBT-P02954,DBT-P02975,DBT-P02979,DBT-P02985,DBT-P02986,DBT-P02998,DBT-P03010,DBT-P03016,DBT-P03019,DBT-P03035,DBT-P03049,DBT-P03054,DBT-P03064,DBT-P03068,DBT-P03075,DBT-P03083,DBT-P03108,DBT-P03119,DBT-P03122,DBT-P03124,DBT-P03151,DBT-P03187,DBT-P03196,DBT-P03220,DBT-P03226,DBT-P03254,DBT-P03273,DBT-P03283,DBT-P03304,DBT-P03332,DBT-P03338,DBT-P03354,DBT-P03366,DBT-P03371,DBT-P03372,DBT-P03376,DBT-P03398,DBT-P03430,DBT-P03468,DBT-P03488,DBT-P03508,DBT-P03519,DBT-P03536,DBT-P03538,DBT-P03541,DBT-P03545,DBT-P03562,DBT-P03571,DBT-P03583,DBT-P03591,DBT-P03614,DBT-P03636,DBT-P03661,DBT-P03678,DBT-P03679,DBT-P03695,DBT-P03696,DBT-P03711,DBT-P03732,DBT-P03749,DBT-P03766,DBT-P03774,DBT-P03789,DBT-P03796,DBT-P03812,DBT-P03818,DBT-P03820,DBT-P03842,DBT-P03844,DBT-P03845,DBT-P03854,DBT-P03864,DBT-P03918,DBT-P03952,DBT-P03963,DBT-P03964,DBT-P03971,DBT-P03987,DBT-P04003,DBT-P04046,DBT-P04053,DBT-P04063,DBT-P04071,DBT-P04085,DBT-P04115,DBT-P04126,DBT-P04131,DBT-P04141,DBT-P04145,DBT-P04154,DBT-P04161,DBT-P04167,DBT-P04176,DBT-P04184,DBT-P04187,DBT-P04195,DBT-P04209,DBT-P04212,DBT-P04218,DBT-P04243,DBT-P04255,DBT-P04260,DBT-P04286,DBT-P04287,DBT-P04304,DBT-P04312,DBT-P04350,DBT-P04373,DBT-P04374,DBT-P04378,DBT-P04382,DBT-P04391,DBT-P04404,DBT-P04411,DBT-P04413,DBT-P04423,DBT-P04425,DBT-P04439,DBT-P04451,DBT-P04457,DBT-P04465,DBT-P04471,DBT-P04479,DBT-P04499,DBT-P04501,DBT-P04504,DBT-P04559,DBT-P04564,DBT-P04571,DBT-P04588,DBT-P04591,DBT-P04607,DBT-P04619,DBT-P04651,DBT-P04655,DBT-P04674,DBT-P04682,DBT-P04743,DBT-P04770,DBT-P04774,DBT-P04787,DBT-P04788,DBT-P04790,DBT-P04802,DBT-P04825,DBT-P04828,DBT-P04841,DBT-P04844,DBT-P04845,DBT-P04859,DBT-P04861,DBT-P04889,DBT-P04910,DBT-P04935,DBT-P04950,DBT-P04956,DBT-P04972,DBT-P04975,DBT-P04982,DBT-P05003,DBT-P05023,DBT-P05036,DBT-P05049,DBT-P05051&amp;CollectionCriteria=Breast-Cancer-Screening-DBT\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Test set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-test-v2.csv\" download=\"BCS-DBT-file-paths-test-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>Added Phase 2 images and spreadsheets. Previous versions (1,2, and 3) are now \"Phase 1\".<\/p><h3>Version 3: Updated 2021\/01\/15<\/h3><table><colgroup><col \/><col \/><\/colgroup><tbody><tr><th>Data Type<\/th><th>Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Test set - Images (DICOM, 47 GB)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-Challenge-Test.tcia\" download=\"BCS-DBT-Challenge-Test.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?saved-cart=nbia-78441610719428450\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Test set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-test.csv\" download=\"BCS-DBT-file-paths-test.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Validation set - Images (DICOM, 28 GB)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-Challenge-Validation.tcia\" download=\"BCS-DBT-Challenge-Validation.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;PatientCriteria=DBT-P00002,DBT-P00114,DBT-P00160,DBT-P00176,DBT-P00213,DBT-P00238,DBT-P00351,DBT-P00385,DBT-P00431,DBT-P00450,DBT-P00547,DBT-P00549,DBT-P00577,DBT-P00651,DBT-P00655,DBT-P00697,DBT-P00714,DBT-P00718,DBT-P00843,DBT-P00844,DBT-P00864,DBT-P00866,DBT-P01150,DBT-P01152,DBT-P01153,DBT-P01173,DBT-P01207,DBT-P01226,DBT-P01249,DBT-P01293,DBT-P01296,DBT-P01305,DBT-P01334,DBT-P01356,DBT-P01490,DBT-P01496,DBT-P01536,DBT-P01538,DBT-P01700,DBT-P01746,DBT-P01772,DBT-P01778,DBT-P01849,DBT-P01851,DBT-P01941,DBT-P01957,DBT-P01967,DBT-P02072,DBT-P02111,DBT-P02117,DBT-P02160,DBT-P02170,DBT-P02214,DBT-P02226,DBT-P02314,DBT-P02381,DBT-P02394,DBT-P02498,DBT-P02558,DBT-P02560,DBT-P02598,DBT-P02648,DBT-P02686,DBT-P02766,DBT-P02776,DBT-P02816,DBT-P02864,DBT-P02898,DBT-P02959,DBT-P02989,DBT-P03027,DBT-P03092,DBT-P03110,DBT-P03113,DBT-P03129,DBT-P03215,DBT-P03263,DBT-P03343,DBT-P03360,DBT-P03390,DBT-P03403,DBT-P03448,DBT-P03501,DBT-P03574,DBT-P03598,DBT-P03605,DBT-P03621,DBT-P03628,DBT-P03651,DBT-P03689,DBT-P03694,DBT-P03728,DBT-P03831,DBT-P03901,DBT-P04008,DBT-P04045,DBT-P04087,DBT-P04097,DBT-P04105,DBT-P04106,DBT-P04208,DBT-P04257,DBT-P04295,DBT-P04344,DBT-P04346,DBT-P04380,DBT-P04396,DBT-P04577,DBT-P04596,DBT-P04622,DBT-P04695,DBT-P04803,DBT-P04808,DBT-P04837,DBT-P04884,DBT-P05018,DBT-P05060&amp;CollectionCriteria=Breast-Cancer-Screening-DBT\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Validation set - Image paths for patients\/studies\/views (csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-validation.csv\" download=\"BCS-DBT-file-paths-validation.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td><p>Training set - Images (DICOM, 186 GB)<\/p><\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/DBT-Challenge-Train.tcia\" download=\"DBT-Challenge-Train.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?saved-cart=nbia-3321609785776535\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Training set - Image paths for patients\/studies\/views (csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-train.csv\" download=\"BCS-DBT-file-paths-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Boxes indicating lesion locations\u00a0(csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-boxes-train-v2.csv\" download=\"BCS-DBT-boxes-train-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups)\u00a0(csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-labels-train.csv\" download=\"BCS-DBT-labels-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>Test set - Images added.<\/p><p>Test set -\u00a0Image paths for patients\/studies\/views spreadsheet added.<\/p><h3>Version 2: Updated 2021\/01\/04<\/h3><table><colgroup><col \/><col \/><\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Validation set - Images (DICOM, 28 GB)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-Challenge-Validation.tcia\" download=\"BCS-DBT-Challenge-Validation.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;PatientCriteria=DBT-P00002,DBT-P00114,DBT-P00160,DBT-P00176,DBT-P00213,DBT-P00238,DBT-P00351,DBT-P00385,DBT-P00431,DBT-P00450,DBT-P00547,DBT-P00549,DBT-P00577,DBT-P00651,DBT-P00655,DBT-P00697,DBT-P00714,DBT-P00718,DBT-P00843,DBT-P00844,DBT-P00864,DBT-P00866,DBT-P01150,DBT-P01152,DBT-P01153,DBT-P01173,DBT-P01207,DBT-P01226,DBT-P01249,DBT-P01293,DBT-P01296,DBT-P01305,DBT-P01334,DBT-P01356,DBT-P01490,DBT-P01496,DBT-P01536,DBT-P01538,DBT-P01700,DBT-P01746,DBT-P01772,DBT-P01778,DBT-P01849,DBT-P01851,DBT-P01941,DBT-P01957,DBT-P01967,DBT-P02072,DBT-P02111,DBT-P02117,DBT-P02160,DBT-P02170,DBT-P02214,DBT-P02226,DBT-P02314,DBT-P02381,DBT-P02394,DBT-P02498,DBT-P02558,DBT-P02560,DBT-P02598,DBT-P02648,DBT-P02686,DBT-P02766,DBT-P02776,DBT-P02816,DBT-P02864,DBT-P02898,DBT-P02959,DBT-P02989,DBT-P03027,DBT-P03092,DBT-P03110,DBT-P03113,DBT-P03129,DBT-P03215,DBT-P03263,DBT-P03343,DBT-P03360,DBT-P03390,DBT-P03403,DBT-P03448,DBT-P03501,DBT-P03574,DBT-P03598,DBT-P03605,DBT-P03621,DBT-P03628,DBT-P03651,DBT-P03689,DBT-P03694,DBT-P03728,DBT-P03831,DBT-P03901,DBT-P04008,DBT-P04045,DBT-P04087,DBT-P04097,DBT-P04105,DBT-P04106,DBT-P04208,DBT-P04257,DBT-P04295,DBT-P04344,DBT-P04346,DBT-P04380,DBT-P04396,DBT-P04577,DBT-P04596,DBT-P04622,DBT-P04695,DBT-P04803,DBT-P04808,DBT-P04837,DBT-P04884,DBT-P05018,DBT-P05060&amp;CollectionCriteria=Breast-Cancer-Screening-DBT\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Validation set - Image paths for patients\/studies\/views (csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-validation.csv\" download=\"BCS-DBT-file-paths-validation.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td><p>Training set - Images (DICOM, 186 GB)<\/p><\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/DBT-Challenge-Train.tcia\" download=\"DBT-Challenge-Train.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?saved-cart=nbia-3321609785776535\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-train.csv\" download=\"BCS-DBT-file-paths-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Training set - Boxes indicating lesion locations\u00a0(csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-boxes-train-v2.csv\" download=\"BCS-DBT-boxes-train-v2.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups)\u00a0(csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-labels-train.csv\" download=\"BCS-DBT-labels-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>Validation set - Images added.<\/p><p>Validation set - Image paths for patients\/studies\/views spreadsheet added.<\/p><p>Volume Slices column added to the <em>Training set - Boxes indicating lesion locations<\/em> spreadsheet.<\/p><h3>Version 1: Updated 2020\/12\/14<\/h3><table><colgroup><col \/><col \/><\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\"><p>Training set - Images (DICOM, 186 GB)<\/p><\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/DBT-Challenge-Train.tcia\" download=\"DBT-Challenge-Train.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?saved-cart=nbia-3321609785776535\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td>Training set - Image paths for patients\/studies\/views (csv)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-file-paths-train.csv\" download=\"BCS-DBT-file-paths-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Training set - Boxes indicating lesion locations\u00a0(csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-boxes-train.csv\" download=\"BCS-DBT-boxes-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups)\u00a0(csv)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"\/wp-content\/uploads\/BCS-DBT-labels-train.csv\" download=\"BCS-DBT-labels-train.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains <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":"5","collection_title":"Breast Cancer Screening - Digital Breast Tomosynthesis","date_updated":"2024-01-18","related_collection":false,"subjects":"5060","analysis_results":"","collection_short_title":"Breast-Cancer-Screening-DBT","data_types":["MG"],"version_change_log":"Added spreadsheets:\r\n<ul>\r\n \t<li>Validation set - Spreadsheet indicating which group each case belongs to (see the paper for details on the groups).<\/li>\r\n \t<li>Validation set - Boxes indicating lesion locations.<\/li>\r\n \t<li>Test set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups)<\/li>\r\n \t<li>Test set - Boxes indicating lesion locations<\/li>\r\n<\/ul>","collection_browse_title":"Breast-Cancer-Screening-DBT","detailed_description":"<h3>Dataset Split Details<\/h3>\r\nThis dataset was originally used for the DBTex2 challenge (<a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex2\/\">https:\/\/www.aapm.org\/GrandChallenge\/DBTex2\/<\/a>), which contains a total of\u00a022032\u00a0breast tomosynthesis scans from\u00a05060\u00a0patients from this collection.\u00a0 The dataset was broken down into the following cohorts for usage at the competition, a split that is maintained for this general release:\r\n<ol>\r\n \t<li>Total:\u00a022032\u00a0scans<\/li>\r\n \t<li>Training:\u00a019148\u00a0scans<\/li>\r\n \t<li>Validation:\u00a01163\u00a0scans<\/li>\r\n \t<li>Test:\u00a01721\u00a0scans<\/li>\r\n<\/ol>\r\nVersion 1, 2 and 3 of the page had a fraction of each of these subsets released, to accompany phase 1 of the aforementioned competition, DBTex.\r\n<h4><strong>Training set (with truth):\u00a0<\/strong><\/h4>\r\nThe training set consists of\u00a019148\u00a0cases. This dataset will be representative of the technical properties (equipment, acquisition parameters, file format) and the nature of lesions in the validation and test sets. An associated Excel file in CSV format will include DBT scan identifier and the definition of the bounding box of all lesions.\r\n<h4><strong>Validation set (without truth):\u00a0<\/strong><\/h4>\r\nThe validation set consists of 1163 cases.\r\n<h4><strong>Test set (without truth):\u00a0<\/strong><\/h4>\r\nThe test set consists of 1721 cases.\r\n<h4><strong>FREQUENTLY ASKED QUESTIONS<\/strong><\/h4>\r\nBelow are some curated important questions for this dataset along with answers from\u00a0<a href=\"https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/\">https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/<\/a>. For more discussion, visit\u00a0<a href=\"https:\/\/www.reddit.com\/r\/DukeDBTData\/\">https:\/\/www.reddit.com\/r\/DukeDBTData\/<\/a>.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Is there a code repository for reading images, drawing bounding boxes, and helper functions\u00a0related to this database?\r\n\r\n<strong>Answer:<\/strong>\u00a0Yes. Please use the following link:\u00a0<a href=\"https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\">https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data<\/a>\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Could you explain the image files\/format present for each study?\r\n\r\n<strong>Answer:<\/strong>\u00a0One DICOM file or image consists of an entire 3D volume (view). These images are stored in compressed DICOM format.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Which software\/tools can I use to read the images?\r\n\r\n<strong>Answer:<\/strong>\u00a0You may use a variety of software packages to read the images. We successfully opened the images with the following software: 3D Slicer, ITK-SNAP, Radiant, MicroDICOM, Matlab, and GDCM.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Do I need to know the pre-processing steps, provided in the code repository, for the images?\r\n\r\n<strong>Answer:<\/strong>\u00a0It is important to look at the pre-processing steps we provided in the code repository. Please see the Python functions for reading image data from a DICOM file into 3D array of pixel values in the proper orientation and for displaying \u201ctruth\u201d boxes (if present). Please also see the readme file there for instructions (<a href=\"https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\">https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data<\/a>). This is crucial as some of the image headers contain incorrect laterality or orientation. For these images, the reference standard \u201ctruth\u201d boxes are provided with respect to the corrected image orientation.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Are 4 views available for every study?\r\n\r\n<strong>Answer:<\/strong>\u00a0Though 4 views (2 per breast, craniocaudal and mediolateral oblique) are present for most of the studies, some exams have fewer than 4 views.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0What kind of encoding is used in the columns of the file \u2018BCS-DBT labels-train-v2.csv\u2019?\r\n\r\n<strong>Answer:<\/strong> The columns \u201cCancer\u201d, \u201cBenign\u201d, \u201cActionable\u201d, \u201cNormal\u201d represent one-hot encoded assignment to a category. Details pertaining to these categories can be found in the Section 2.1: <a href=\"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100\">https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a><a href=\"https:\/\/arxiv.org\/pdf\/2011.07995.pdf\">.<\/a>\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0How to interpret the \u201cSlice\u201d column in the data provided in the file \u2018BCS-DBT boxes-train-v2.csv\u2019?\r\n\r\n<strong>Answer:<\/strong> A: The \u201cSlice\u201d column corresponds to the central slice of a biopsied lesion. More details on the image annotation are provided in the paper <a href=\"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100\">https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a>\u00a0(section\u00a02.1.2). For evaluation, we assume that lesions span 25% of volume slices in each direction. It is reflected in the evaluation functions available on GitHub:\u00a0<a href=\"https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\/blob\/master\/duke_dbt_data.py\">https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\/blob\/master\/duke_dbt_data.py<\/a>\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0How to interpret the columns of \u2018BCS-DBT boxes-train-v2.csv\u2019?\r\n\r\n<strong>Answer:<\/strong>\r\n<ul>\r\n \t<li>PatientID: string \u2013 patient identifier<\/li>\r\n \t<li>StudyUID: string \u2013 study identifier<\/li>\r\n \t<li>View: string \u2013 view name, one of: RLL, LCC, RMLO, LMLO\u00a0(you might see a numerical suffix after these if multiple images under one view\u00a0are\u00a0present)<\/li>\r\n \t<li>Subject:\u00a0integer\u00a0\u2013 encodes\u00a0a radiologist who performed annotation<\/li>\r\n \t<li>Slice: integer- the central slice of a biopsied lesion<\/li>\r\n \t<li>X: integer \u2013 X coordinate (on the horizontal axis) of the left edge of the predicted bounding box in 0-based indexing (for the left-most column of the image x=0)<\/li>\r\n \t<li>Y: integer \u2013 Y coordinate (on the vertical axis) of the top edge of the predicted bounding box in 0-based indexing (for the top-most row of the image y=0)<\/li>\r\n \t<li>Width: integer \u2013 predicted bounding box width (along the horizontal axis)<\/li>\r\n \t<li>Height: integer \u2013 predicted bounding box height (along the vertical axis)<\/li>\r\n \t<li>Class:\u00a0string\u00a0\u2013 either benign or cancer<\/li>\r\n \t<li>AD:\u00a0integer\u00a0\u2013\u00a01 if architectural distortion is present, else 0<\/li>\r\n \t<li>VolumeSlices: integer \u2013\u00a0The total number of slices in volume containing the bounding box (used in evaluation function)<\/li>\r\n<\/ul>\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Why are the number of bounding boxes much less than the number of training samples?\r\n\r\n<strong>Answer:<\/strong> The bounding boxes are applicable only to cases with biopsy-proven benign and cancer findings. The training set consists of cases with normal, actionable, benign, and cancer findings. For details on these categories, see Section 2.1: <a href=\"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100\">https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a>.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong> Why can't I find the path of a downloaded image in the csv file \u201cTraining set \u2013 Image paths for patients\/studies\/views (csv)\u201d?\r\n\r\n<strong>Answer:<\/strong>\u00a0At times, you may need to replace \u201c\\\u201d with \u201c\/\u201d in the path of an image file to find the path in the csv file \u201cTraining set \u2013 Image paths for patients\/studies\/views (csv)\u201d.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Why are there both \u201cdescriptive_path\u201d and \u201cclassic_path\u201d in the .csv file \u201d Training set \u2013 Image paths for patients\/studies\/views (.csv)\u201d?\r\n\r\n<strong>Answer:<\/strong>\u00a0When you download our data using the NBIA Data Retriever, there are two options (\u201cDescriptive Directory Name\u201d and \u201cClassic Directory Name\u201d) for selecting the Directory Type, that correspond to those two paths in the .csv file.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Does the dataset contain microcalcifications?\r\n\r\n<strong>Answer:<\/strong>\u00a0It contains microcalcifications. However, they were not annotated and were not the cause for actionability or biopsy.\r\n\r\n<hr \/>\r\n\r\n<strong>Question:<\/strong>\u00a0Could you provide some examples of markers in the images?\r\n\r\n<strong>Answer:<\/strong> Yes. Some examples are listed below. The images or their parts are taken from this collection under the CC BY-NC 4.0 license (<a href=\"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/\">https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/<\/a>).\r\n\r\n1. Circle for a raised area on the skin such as a mole (image 13345.000000-24122 from this collection)\r\n\r\n<a href=\"\/wp-content\/uploads\/Circle-for-raised-area.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignleft wp-image-260 size-medium\" src=\"\/wp-content\/uploads\/Circle-for-raised-area.png\" alt=\"Circle for a raised area\" width=\"286\" height=\"349\" \/><\/a>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n2. Line for a previous surgery (image 14338.000000-72252 from this collection)\r\n\r\n<a href=\"\/wp-content\/uploads\/Surgery-Line.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignleft wp-image-259 size-medium\" src=\"\/wp-content\/uploads\/Surgery-Line.png\" alt=\"Line for a previous surgery\" width=\"288\" height=\"283\" \/><\/a>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n3. Solid pellet for the nipple (image 8764.000000-63613 from this collection)\r\n\r\n<a href=\"\/wp-content\/uploads\/Solid-Pellet.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignleft wp-image-258 size-medium\" src=\"\/wp-content\/uploads\/Solid-Pellet.png\" alt=\"Solid pellet for the nipple\" width=\"271\" height=\"363\" \/><\/a>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n4. Markers for locations of pain (one or more spots, image 20566.000000-32081 from this collection)\r\n<a href=\"\/wp-content\/uploads\/Markers.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignleft wp-image-257 size-medium\" src=\"\/wp-content\/uploads\/Markers.png\" alt=\"Markers for locations of pain\" width=\"282\" height=\"314\" \/><\/a>","supporting_data":["Clinical","Software\/Source Code"],"collection_featured_image":false,"collection_summary":"<p>Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.<\/p><p>Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.\u00a0 The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) Image paths for patients\/studies\/views.\u00a0 A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:<\/p><p>M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. \u015awi\u0119cicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model.\u00a0(<a href=\"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100\">https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a>)<em>.<\/em><\/p><p>Additional information and resources related to this dataset can be found here:\u00a0<a href=\"https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/\">https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/<\/a><\/p><p>A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.<\/p><p>Please visit this discussion forum for any questions related to the data: <a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=https-3A__www.reddit.com_r_DukeDBTData_&amp;d=DwMF-g&amp;c=27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&amp;r=ToZnZI1LUBsGvnucRq6iJA&amp;m=y-us0hreJNHSpZuajGevDhjO5IfTJyDtfsdCkuiZ4iA&amp;s=niaoetWU-hYXmGtnzzk_85ZEjn_q0t-TOwdi7Fr-qt8&amp;e=\">https:\/\/www.reddit.com\/r\/DukeDBTData\/<\/a><\/p><h3>Required Preprocessing of DBT Images<\/h3><p>For some of the images, the laterality stored in the DICOM header and\/or image orientation are incorrect. The reference standard \"truth\" boxes are defined with respect to the\u00a0<strong>corrected<\/strong>\u00a0image orientation in these instances. Therefore, it is\u00a0<strong>crucial<\/strong>\u00a0to provide your results for images in the correct image orientation. Python functions for loading image data from a DICOM file into 3D array of pixel values in the correct orientation and for displaying \"truth\" boxes (if any) are on\u00a0<a href=\"https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data\">GitHub<\/a>. Please see the readme file there for instructions.<\/p><h3>DBTex Lesion Detection Challenge Predictions<\/h3><p>The\u00a0DBTex lesion detection challenge tasked participating teams with detecting lesions in the BCS-DBT test set. The challenge had two phases:\u00a0<a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex\/\">DBTex1<\/a>\u00a0and\u00a0<a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex2\/\">DBTex2<\/a>.\u00a0<strong><a href=\"\/wp-content\/uploads\/team_predictions_bothphases.zip\" download=\"team_predictions_bothphases.zip\">Here<\/a><\/strong>\u00a0we provide the BCS-DBT lesion predictions made by all participating teams for both phases, for both the BCS-DBT test and validation sets, as \u201cteam_predictions_bothphases.zip\u201d. Please see\u00a0<a href=\"https:\/\/www.aapm.org\/GrandChallenge\/DBTex2\/\">here<\/a>\u00a0under \u201c<strong>Output format for the DBTex2 Challenge test set results<\/strong>\u201d for a description of how these results are formatted. Finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above \u201c<strong>Required Preprocessing of DBT Images<\/strong>\u201d.<\/p><p>If you use these predictions, please reference the DBTex challenge paper:<\/p><p>Konz N, Buda M, Gu H, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. <em>JAMA Netw Open.<\/em>\u00a02023;6(2):e230524. doi:10.1001\/jamanetworkopen.2023.0524<\/p>","collection_acknowledgements":"<p>We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p><ul><li><p>Duke University Hospital\/Duke University,\u00a0Durham, NC, USA<\/p><\/li><li><p>We would like to acknowledge all those who contributed to the curation of this dataset<\/p><\/li><li><p>This work was supported by a grant from the NIH: 1 R01 EB021360 (PI: Mazurowski).<\/p><\/li><\/ul>","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41599","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":2,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41599\/revisions"}],"predecessor-version":[{"id":49727,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41599\/revisions\/49727"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=41599"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}