{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VOSGOKBEJVQZZ7KARLEXTTHV2N","short_pith_number":"pith:VOSGOKBE","schema_version":"1.0","canonical_sha256":"aba46728244d619cfd408ac979ccf5d3461db56fed46be0142d8db0612fb5250","source":{"kind":"arxiv","id":"1802.08080","version":2},"attestation_state":"computed","paper":{"title":"Classification of Breast Cancer Histology using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Golatkar, Amit Sethi, Deepak Anand","submitted_at":"2018-02-22T14:56:38Z","abstract_excerpt":"Breast Cancer is a major cause of death worldwide among women. Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al. These images are to be classified into four classes namely, i) normal tissue, ii) benign tumor, iii) in-situ carcinoma and iv) invasive carcinoma. Our strate"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1802.08080","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T14:56:38Z","cross_cats_sorted":[],"title_canon_sha256":"a463e88a410a07e78316fdd6df420e36412152b4436c5f69bac7a6a5a495d17e","abstract_canon_sha256":"43814d46abd0ff09ed45e329bf50d045fc5f8ba47e395b2965ce68e72cd4994a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:52.629466Z","signature_b64":"ywgPtbixZbMZpOILYLs1H3SegbvwJhLceHT+Mp1qbl1HoDhdgXYoGaiRpps9UMQ2Zt9vEfIXG5vIJ6gbFaMcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aba46728244d619cfd408ac979ccf5d3461db56fed46be0142d8db0612fb5250","last_reissued_at":"2026-05-18T00:09:52.628889Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:52.628889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classification of Breast Cancer Histology using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Golatkar, Amit Sethi, Deepak Anand","submitted_at":"2018-02-22T14:56:38Z","abstract_excerpt":"Breast Cancer is a major cause of death worldwide among women. Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al. These images are to be classified into four classes namely, i) normal tissue, ii) benign tumor, iii) in-situ carcinoma and iv) invasive carcinoma. Our strate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08080","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1802.08080","created_at":"2026-05-18T00:09:52.628988+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08080v2","created_at":"2026-05-18T00:09:52.628988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08080","created_at":"2026-05-18T00:09:52.628988+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOSGOKBEJVQZ","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOSGOKBEJVQZZ7KA","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOSGOKBE","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N","json":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N.json","graph_json":"https://pith.science/api/pith-number/VOSGOKBEJVQZZ7KARLEXTTHV2N/graph.json","events_json":"https://pith.science/api/pith-number/VOSGOKBEJVQZZ7KARLEXTTHV2N/events.json","paper":"https://pith.science/paper/VOSGOKBE"},"agent_actions":{"view_html":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N","download_json":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N.json","view_paper":"https://pith.science/paper/VOSGOKBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08080&json=true","fetch_graph":"https://pith.science/api/pith-number/VOSGOKBEJVQZZ7KARLEXTTHV2N/graph.json","fetch_events":"https://pith.science/api/pith-number/VOSGOKBEJVQZZ7KARLEXTTHV2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N/action/storage_attestation","attest_author":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N/action/author_attestation","sign_citation":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N/action/citation_signature","submit_replication":"https://pith.science/pith/VOSGOKBEJVQZZ7KARLEXTTHV2N/action/replication_record"}},"created_at":"2026-05-18T00:09:52.628988+00:00","updated_at":"2026-05-18T00:09:52.628988+00:00"}