{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BGNKLMFWNA52SQB2CYPPNENFGJ","short_pith_number":"pith:BGNKLMFW","schema_version":"1.0","canonical_sha256":"099aa5b0b6683ba9403a161ef691a53266fab3bb3230cbfd3561533e4c9edad6","source":{"kind":"arxiv","id":"2412.13156","version":1},"attestation_state":"computed","paper":{"title":"S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alison D. Gernand, James Z. Wang, Jeffery A. Goldstein, Sitao Zhang, Yimu Pan","submitted_at":"2024-12-17T18:30:22Z","abstract_excerpt":"Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored training procedures -- can alleviate these issues, they depend on the availability and reliability of domain knowledge. When such knowledge is unavailable, misleading, or improperly applied, performance may deteriorate. In response, we introduce a novel, domain-agnostic, add-on, an"},"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":"2412.13156","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-17T18:30:22Z","cross_cats_sorted":[],"title_canon_sha256":"2ba34a2b4c6a00bb39b3dd3fe469558f3d07c4de6a5d349532c3f4507e3fac70","abstract_canon_sha256":"fd9763ce234e19b40bef4f07141175336db0dbce082dec7152bb9192f0dda1d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:41.174875Z","signature_b64":"2awX+a84YxuQcC06gjNyLDRVoYbtdzKveRcHc8I9yEAUz8sWjxyRKwEYeGmy2bx79cBtOFUDhZEEBEih8LEVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"099aa5b0b6683ba9403a161ef691a53266fab3bb3230cbfd3561533e4c9edad6","last_reissued_at":"2026-07-05T09:50:41.174269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:41.174269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alison D. Gernand, James Z. Wang, Jeffery A. Goldstein, Sitao Zhang, Yimu Pan","submitted_at":"2024-12-17T18:30:22Z","abstract_excerpt":"Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored training procedures -- can alleviate these issues, they depend on the availability and reliability of domain knowledge. When such knowledge is unavailable, misleading, or improperly applied, performance may deteriorate. In response, we introduce a novel, domain-agnostic, add-on, an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13156","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/2412.13156/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":"2412.13156","created_at":"2026-07-05T09:50:41.174369+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13156v1","created_at":"2026-07-05T09:50:41.174369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13156","created_at":"2026-07-05T09:50:41.174369+00:00"},{"alias_kind":"pith_short_12","alias_value":"BGNKLMFWNA52","created_at":"2026-07-05T09:50:41.174369+00:00"},{"alias_kind":"pith_short_16","alias_value":"BGNKLMFWNA52SQB2","created_at":"2026-07-05T09:50:41.174369+00:00"},{"alias_kind":"pith_short_8","alias_value":"BGNKLMFW","created_at":"2026-07-05T09:50:41.174369+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.03838","citing_title":"IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ","json":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ.json","graph_json":"https://pith.science/api/pith-number/BGNKLMFWNA52SQB2CYPPNENFGJ/graph.json","events_json":"https://pith.science/api/pith-number/BGNKLMFWNA52SQB2CYPPNENFGJ/events.json","paper":"https://pith.science/paper/BGNKLMFW"},"agent_actions":{"view_html":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ","download_json":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ.json","view_paper":"https://pith.science/paper/BGNKLMFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13156&json=true","fetch_graph":"https://pith.science/api/pith-number/BGNKLMFWNA52SQB2CYPPNENFGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/BGNKLMFWNA52SQB2CYPPNENFGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ/action/storage_attestation","attest_author":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ/action/author_attestation","sign_citation":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ/action/citation_signature","submit_replication":"https://pith.science/pith/BGNKLMFWNA52SQB2CYPPNENFGJ/action/replication_record"}},"created_at":"2026-07-05T09:50:41.174369+00:00","updated_at":"2026-07-05T09:50:41.174369+00:00"}