{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:P5FKNP4P2ZJR2NTAESIBYWXJLM","short_pith_number":"pith:P5FKNP4P","schema_version":"1.0","canonical_sha256":"7f4aa6bf8fd6531d366024901c5ae95b15510f0297f9295b37d88ce3c5971c51","source":{"kind":"arxiv","id":"1706.03702","version":1},"attestation_state":"computed","paper":{"title":"Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam P. Harrison, Daniel J. Mollura, Kevin George, Le Lu, Ronald M. Summers, Ziyue Xu","submitted_at":"2017-06-12T15:54:35Z","abstract_excerpt":"Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a pre-requisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottom-up deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progr"},"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":"1706.03702","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:54:35Z","cross_cats_sorted":[],"title_canon_sha256":"1048e96239863d23f7bf878cba95640fb4c738759731c70af539001fd1721b51","abstract_canon_sha256":"1f5191f10deb4099749882383b5eff1ecad50e23f79a48488803ea0c3c3dbab1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:04.388149Z","signature_b64":"6enIyE62fWNXtarEbixcxAYz3Z7RHMQ0OgWxTtsSByjHTl1BX4Zb1HV1K2pZJ6fpaLDiJofBWtcAavhcCbyWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f4aa6bf8fd6531d366024901c5ae95b15510f0297f9295b37d88ce3c5971c51","last_reissued_at":"2026-05-18T00:24:04.387635Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:04.387635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam P. Harrison, Daniel J. Mollura, Kevin George, Le Lu, Ronald M. Summers, Ziyue Xu","submitted_at":"2017-06-12T15:54:35Z","abstract_excerpt":"Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a pre-requisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottom-up deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03702","kind":"arxiv","version":1},"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":"1706.03702","created_at":"2026-05-18T00:24:04.387721+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03702v1","created_at":"2026-05-18T00:24:04.387721+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03702","created_at":"2026-05-18T00:24:04.387721+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5FKNP4P2ZJR","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5FKNP4P2ZJR2NTA","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5FKNP4P","created_at":"2026-05-18T12:31:37.085036+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/P5FKNP4P2ZJR2NTAESIBYWXJLM","json":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM.json","graph_json":"https://pith.science/api/pith-number/P5FKNP4P2ZJR2NTAESIBYWXJLM/graph.json","events_json":"https://pith.science/api/pith-number/P5FKNP4P2ZJR2NTAESIBYWXJLM/events.json","paper":"https://pith.science/paper/P5FKNP4P"},"agent_actions":{"view_html":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM","download_json":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM.json","view_paper":"https://pith.science/paper/P5FKNP4P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03702&json=true","fetch_graph":"https://pith.science/api/pith-number/P5FKNP4P2ZJR2NTAESIBYWXJLM/graph.json","fetch_events":"https://pith.science/api/pith-number/P5FKNP4P2ZJR2NTAESIBYWXJLM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM/action/storage_attestation","attest_author":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM/action/author_attestation","sign_citation":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM/action/citation_signature","submit_replication":"https://pith.science/pith/P5FKNP4P2ZJR2NTAESIBYWXJLM/action/replication_record"}},"created_at":"2026-05-18T00:24:04.387721+00:00","updated_at":"2026-05-18T00:24:04.387721+00:00"}