{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-convolutional-neural-networks-for-breast","title":"Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis","arxiv_id":"1802.00752","date":"2018-02-02","proceeding":null,"authors":["Alexander Rakhlin","Alexey Shvets","Vladimir Iglovikov","Alexandr A. Kalinin"],"abstract":"Breast cancer is one of the main causes of cancer death worldwide. Early\ndiagnostics significantly increases the chances of correct treatment and\nsurvival, but this process is tedious and often leads to a disagreement between\npathologists. Computer-aided diagnosis systems showed potential for improving\nthe diagnostic accuracy. In this work, we develop the computational approach\nbased on deep convolution neural networks for breast cancer histology image\nclassification. Hematoxylin and eosin stained breast histology microscopy image\ndataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast\nCancer Histology Images. Our approach utilizes several deep neural network\narchitectures and gradient boosted trees classifier. For 4-class classification\ntask, we report 87.2% accuracy. For 2-class classification task to detect\ncarcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity\n96.5/88.0% at the high-sensitivity operating point. To our knowledge, this\napproach outperforms other common methods in automated histopathological image\nclassification. The source code for our approach is made publicly available at\nhttps://github.com/alexander-rakhlin/ICIAR2018","url_abs":"http://arxiv.org/abs/1802.00752v2","url_pdf":"http://arxiv.org/pdf/1802.00752v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-convolutional-neural-networks-for-breast","repo_url":"https://github.com/alexander-rakhlin/ICIAR2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-convolutional-neural-networks-for-breast","repo_url":"https://github.com/liupeng0606/voting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-convolutional-neural-networks-for-breast","repo_url":"https://github.com/vavaidya/breast-cancer-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"histopathological-image-classification","task_name":"Histopathological Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.00752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.00752"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alexander-rakhlin/ICIAR2018","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vavaidya/breast-cancer-detection","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/liupeng0606/voting","reach":{"status":"unanswered"}}],"summary":{"ran_fixture":3,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1},"listed":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"55528ffe915a2cb1","entry":"combine_model_scores","repo":"alexander-rakhlin/ICIAR2018","repo_kind":"official","path":"crossvalidate_blending.py","file_url":"https://github.com/alexander-rakhlin/ICIAR2018/blob/HEAD/crossvalidate_blending.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"55528ffe915a2cb1"}},{"code_sha256_prefix":"21cfedf3ffb6fdaa","entry":"hematoxylin_eosin_aug","repo":"vavaidya/breast-cancer-detection","repo_kind":"listed","path":"ICIAR2018/feature_extractor.py","file_url":"https://github.com/vavaidya/breast-cancer-detection/blob/HEAD/ICIAR2018/feature_extractor.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"21cfedf3ffb6fdaa"}},{"code_sha256_prefix":"4c7a8277f6a50e09","entry":"normalize_staining","repo":"vavaidya/breast-cancer-detection","repo_kind":"listed","path":"ICIAR2018/feature_extractor.py","file_url":"https://github.com/vavaidya/breast-cancer-detection/blob/HEAD/ICIAR2018/feature_extractor.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4c7a8277f6a50e09"}},{"code_sha256_prefix":"aa04f8cb7595031f","entry":"recursive_glob","repo":"vavaidya/breast-cancer-detection","repo_kind":"listed","path":"ICIAR2018/feature_extractor.py","file_url":"https://github.com/vavaidya/breast-cancer-detection/blob/HEAD/ICIAR2018/feature_extractor.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"aa04f8cb7595031f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}