{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3RLT7QUWS6OT3NQFG7HWKFDKVQ","short_pith_number":"pith:3RLT7QUW","schema_version":"1.0","canonical_sha256":"dc573fc296979d3db60537cf65146aac37f9ae0455a9f8f52d579b8cc1333385","source":{"kind":"arxiv","id":"1812.07166","version":2},"attestation_state":"computed","paper":{"title":"Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bjoern H Menze, Hongwei Li, Jiechao Ma, Rongguo Zhang, Sen Liang, Wei-Shi Zheng, Xiang Li","submitted_at":"2018-12-18T04:41:16Z","abstract_excerpt":"Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large"},"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":"1812.07166","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-18T04:41:16Z","cross_cats_sorted":[],"title_canon_sha256":"efda5c5375f1b50eb9e2ad39adae0bea01f02363d41d1aba49b33625ed564515","abstract_canon_sha256":"99cd7baf59a40ecfed99bc5fbd15af285db42aa521f7ebbfec09d0d0fb8f7fda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:15.082824Z","signature_b64":"JNEfrXsOQQY7W9S4n51OTmXhbDF5TCCbvsQgd0IKlKq7jkzlMa2HOPhNCzysTGYZ10nxat5Qgt9XWJhlAooeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc573fc296979d3db60537cf65146aac37f9ae0455a9f8f52d579b8cc1333385","last_reissued_at":"2026-05-17T23:45:15.082177Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:15.082177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bjoern H Menze, Hongwei Li, Jiechao Ma, Rongguo Zhang, Sen Liang, Wei-Shi Zheng, Xiang Li","submitted_at":"2018-12-18T04:41:16Z","abstract_excerpt":"Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07166","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":"1812.07166","created_at":"2026-05-17T23:45:15.082273+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.07166v2","created_at":"2026-05-17T23:45:15.082273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07166","created_at":"2026-05-17T23:45:15.082273+00:00"},{"alias_kind":"pith_short_12","alias_value":"3RLT7QUWS6OT","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3RLT7QUWS6OT3NQF","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3RLT7QUW","created_at":"2026-05-18T12:32:02.567920+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/3RLT7QUWS6OT3NQFG7HWKFDKVQ","json":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ.json","graph_json":"https://pith.science/api/pith-number/3RLT7QUWS6OT3NQFG7HWKFDKVQ/graph.json","events_json":"https://pith.science/api/pith-number/3RLT7QUWS6OT3NQFG7HWKFDKVQ/events.json","paper":"https://pith.science/paper/3RLT7QUW"},"agent_actions":{"view_html":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ","download_json":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ.json","view_paper":"https://pith.science/paper/3RLT7QUW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.07166&json=true","fetch_graph":"https://pith.science/api/pith-number/3RLT7QUWS6OT3NQFG7HWKFDKVQ/graph.json","fetch_events":"https://pith.science/api/pith-number/3RLT7QUWS6OT3NQFG7HWKFDKVQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ/action/storage_attestation","attest_author":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ/action/author_attestation","sign_citation":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ/action/citation_signature","submit_replication":"https://pith.science/pith/3RLT7QUWS6OT3NQFG7HWKFDKVQ/action/replication_record"}},"created_at":"2026-05-17T23:45:15.082273+00:00","updated_at":"2026-05-17T23:45:15.082273+00:00"}