{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y5FXCUOD6QXJQ4PRHPXH7X22HN","short_pith_number":"pith:Y5FXCUOD","schema_version":"1.0","canonical_sha256":"c74b7151c3f42e9871f13bee7fdf5a3b525a64679eb8b58acb6aba6393ec3004","source":{"kind":"arxiv","id":"2411.12547","version":1},"attestation_state":"computed","paper":{"title":"S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Shuo Hong Wang, Xiang Liu, Xinyi Chen, Yuke Wu, Yunyu Shi, Yuqing Xu, Zhenglei Wang","submitted_at":"2024-11-19T15:00:18Z","abstract_excerpt":"The irregular and challenging characteristics of lung adenocarcinoma nodules in computed tomography (CT) images complicate staging diagnosis, making accurate segmentation critical for clinicians to extract detailed lesion information. In this study, we propose a segmentation model, S3TU-Net, which integrates multi-dimensional spatial connectors and a superpixel-based visual transformer. S3TU-Net is built on a multi-view CNN-Transformer hybrid architecture, incorporating superpixel algorithms, structured weighting, and spatial shifting techniques to achieve superior segmentation performance. Th"},"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":"2411.12547","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-19T15:00:18Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"f36e2987e465f633bd23875d84f8f49426e6848694d980f8812feee55744ad6f","abstract_canon_sha256":"a18e4c6ce5870daaa6b98e236516b89bb3833af7c297830e02a4feba45dc3931"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:44.761714Z","signature_b64":"4ZowweLXHgOGLTOq8izPfiRk1E0FrLZo7c0hMyS3MtQbpSDb0PYl14mHA7xOoXXk471T2SbtCIgd+Cr/68S4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c74b7151c3f42e9871f13bee7fdf5a3b525a64679eb8b58acb6aba6393ec3004","last_reissued_at":"2026-07-05T09:37:44.761246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:44.761246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Shuo Hong Wang, Xiang Liu, Xinyi Chen, Yuke Wu, Yunyu Shi, Yuqing Xu, Zhenglei Wang","submitted_at":"2024-11-19T15:00:18Z","abstract_excerpt":"The irregular and challenging characteristics of lung adenocarcinoma nodules in computed tomography (CT) images complicate staging diagnosis, making accurate segmentation critical for clinicians to extract detailed lesion information. In this study, we propose a segmentation model, S3TU-Net, which integrates multi-dimensional spatial connectors and a superpixel-based visual transformer. S3TU-Net is built on a multi-view CNN-Transformer hybrid architecture, incorporating superpixel algorithms, structured weighting, and spatial shifting techniques to achieve superior segmentation performance. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12547","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.12547/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2411.12547","created_at":"2026-07-05T09:37:44.761303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.12547v1","created_at":"2026-07-05T09:37:44.761303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12547","created_at":"2026-07-05T09:37:44.761303+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y5FXCUOD6QXJ","created_at":"2026-07-05T09:37:44.761303+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y5FXCUOD6QXJQ4PR","created_at":"2026-07-05T09:37:44.761303+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y5FXCUOD","created_at":"2026-07-05T09:37:44.761303+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/Y5FXCUOD6QXJQ4PRHPXH7X22HN","json":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN.json","graph_json":"https://pith.science/api/pith-number/Y5FXCUOD6QXJQ4PRHPXH7X22HN/graph.json","events_json":"https://pith.science/api/pith-number/Y5FXCUOD6QXJQ4PRHPXH7X22HN/events.json","paper":"https://pith.science/paper/Y5FXCUOD"},"agent_actions":{"view_html":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN","download_json":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN.json","view_paper":"https://pith.science/paper/Y5FXCUOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.12547&json=true","fetch_graph":"https://pith.science/api/pith-number/Y5FXCUOD6QXJQ4PRHPXH7X22HN/graph.json","fetch_events":"https://pith.science/api/pith-number/Y5FXCUOD6QXJQ4PRHPXH7X22HN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN/action/storage_attestation","attest_author":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN/action/author_attestation","sign_citation":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN/action/citation_signature","submit_replication":"https://pith.science/pith/Y5FXCUOD6QXJQ4PRHPXH7X22HN/action/replication_record"}},"created_at":"2026-07-05T09:37:44.761303+00:00","updated_at":"2026-07-05T09:37:44.761303+00:00"}