{"url":"/dataset/inbreast","name":"InBreast","full_name":"InBreast","description_markdown":"Rationale and objectives: Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database.\r\n\r\nMaterials and methods: Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used.\r\n\r\n**Results: **The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format.\r\n\r\nConclusion: The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.","description_withheld":null,"homepage":"","introduced_date":"2012-02-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/inbreast-toward-a-full-field-digital","title":"INbreast: toward a full-field digital mammographic database","first_author":"Ines C. Moreira","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Cancer-no cancer per breast classification","url":"/task/cancer-no-cancer-per-breast-classification","datasets_with_task":"/datasets/task/cancer-no-cancer-per-breast-classification"},{"name":"Source Free Object Detection","url":"/task/source-free-object-detection","datasets_with_task":"/datasets/task/source-free-object-detection"},{"name":"Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification","url":"/task/suspicous-birads-45-no-suspicous-birads-123","datasets_with_task":"/datasets/task/suspicous-birads-45-no-suspicous-birads-123"}],"languages":[],"variants":["InBreast"],"data_loaders":[{"repo":"https://github.com/ngohongthong1832004/inBreast","url":"https://github.com/ngohongthong1832004/inBreast","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":107,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/suspicous-birads-45-no-suspicous-birads-123","task":"Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification","dataset_variant":"InBreast","rows":16,"metrics":["AUC"],"first_row_in_archive_order":{"model":"WCCNet DenseNet-121","paper":"/paper/wdccnet-weighted-double-classifier-constraint","metrics":{"AUC":"0.947"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cancer-no-cancer-per-breast-classification-on-1","task":"Cancer-no cancer per breast classification","dataset_variant":"InBreast","rows":3,"metrics":["AUC"],"first_row_in_archive_order":{"model":"PHYSEnet (n=2)","paper":"/paper/multi-view-breast-cancer-classification-via","metrics":{"AUC":"0.814"},"code_links":[{"title":"ispamm/phbreast","url":"https://github.com/ispamm/phbreast"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/source-free-object-detection-on-inbreast","task":"Source Free Object Detection","dataset_variant":"InBreast","rows":3,"metrics":["R@0.05","R@0.3","R@0.5","R@1.0","AUC","F1-score"],"first_row_in_archive_order":{"model":"GT","paper":"/paper/context-aware-grounded-teacher-for-source-1","metrics":{"AUC":"0.589","F1-score":"0.758","R@0.05":"0.06","R@0.3":"0.45","R@0.5":"0.65","R@1.0":"0.92"},"code_links":[{"title":"Tajamul21/Grounded_Teacher","url":"https://github.com/Tajamul21/Grounded_Teacher"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/context-aware-grounded-teacher-for-source-1","title":"Context Aware Grounded Teacher for Source Free Object Detection","date":"2025-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-view-breast-cancer-classification-via","title":"Multi-View Hypercomplex Learning for Breast Cancer Screening","date":"2022-04-12","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/wdccnet-weighted-double-classifier-constraint","title":"WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification","date":"2021-10-04","rows_on_this_dataset":7,"code_links":0,"syntology":null},{"paper":"/paper/exploring-sequence-feature-alignment-for","title":"Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers","date":"2021-07-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-neural-networks-with-region-based","title":"Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification","date":"2020-06-06","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/unbiased-mean-teacher-for-cross-domain-object","title":"Unbiased Mean Teacher for Cross-domain Object Detection","date":"2020-03-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-multi-instance-networks-with-sparse-1","title":"Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification","date":"2017-05-23","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/automated-mass-detection-in-mammograms-using","title":"Automated mass detection in mammograms using cascaded deep learning and random forests,","date":"2015-11-23","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":13,"samples_ran":5,"samples_unverified":8,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}