{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BM6CHOITF6RYNDHJOPOPF2AHMK","short_pith_number":"pith:BM6CHOIT","schema_version":"1.0","canonical_sha256":"0b3c23b9132fa3868ce973dcf2e80762b60afe7f95bb81858be499e6e130bc94","source":{"kind":"arxiv","id":"1908.03204","version":2},"attestation_state":"computed","paper":{"title":"Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Wenshuai Zhao, Zengfeng Zeng","submitted_at":"2019-08-09T02:41:55Z","abstract_excerpt":"U-Net has achieved huge success in various medical image segmentation challenges. Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyper-parameter, but their generalization always can't be guaranteed. Here, we focused on the basic U-Net architecture and proposed a multi scale supervised 3D U-Net for the segmentation task in KiTS19 challenge. To enhance the performance, our work can be summarized as three folds: first, we used multi scale supervision in the decoder pathway, which could encourage the network to predict right results f"},"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":"1908.03204","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-09T02:41:55Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"9bdf94819015f313795c30fd082c8fcd9e9110942e6850f5ad4859328fda228e","abstract_canon_sha256":"dba7693972e4af1901af0e69bfc1b290eb914ce506cd2c404d9a85fad0242972"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:55:13.220885Z","signature_b64":"3hkQFRaazWlswmiyH1sZVxGk4J3rgjxtPmNGNIJTn+ydcxeGWe1uT6A/7Qyje7YP70MZC8tiwTv1q+HVNGMeCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b3c23b9132fa3868ce973dcf2e80762b60afe7f95bb81858be499e6e130bc94","last_reissued_at":"2026-07-04T23:55:13.220405Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:55:13.220405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Wenshuai Zhao, Zengfeng Zeng","submitted_at":"2019-08-09T02:41:55Z","abstract_excerpt":"U-Net has achieved huge success in various medical image segmentation challenges. Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyper-parameter, but their generalization always can't be guaranteed. Here, we focused on the basic U-Net architecture and proposed a multi scale supervised 3D U-Net for the segmentation task in KiTS19 challenge. To enhance the performance, our work can be summarized as three folds: first, we used multi scale supervision in the decoder pathway, which could encourage the network to predict right results f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03204","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.03204/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":"1908.03204","created_at":"2026-07-04T23:55:13.220476+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.03204v2","created_at":"2026-07-04T23:55:13.220476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03204","created_at":"2026-07-04T23:55:13.220476+00:00"},{"alias_kind":"pith_short_12","alias_value":"BM6CHOITF6RY","created_at":"2026-07-04T23:55:13.220476+00:00"},{"alias_kind":"pith_short_16","alias_value":"BM6CHOITF6RYNDHJ","created_at":"2026-07-04T23:55:13.220476+00:00"},{"alias_kind":"pith_short_8","alias_value":"BM6CHOIT","created_at":"2026-07-04T23:55:13.220476+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/BM6CHOITF6RYNDHJOPOPF2AHMK","json":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK.json","graph_json":"https://pith.science/api/pith-number/BM6CHOITF6RYNDHJOPOPF2AHMK/graph.json","events_json":"https://pith.science/api/pith-number/BM6CHOITF6RYNDHJOPOPF2AHMK/events.json","paper":"https://pith.science/paper/BM6CHOIT"},"agent_actions":{"view_html":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK","download_json":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK.json","view_paper":"https://pith.science/paper/BM6CHOIT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.03204&json=true","fetch_graph":"https://pith.science/api/pith-number/BM6CHOITF6RYNDHJOPOPF2AHMK/graph.json","fetch_events":"https://pith.science/api/pith-number/BM6CHOITF6RYNDHJOPOPF2AHMK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK/action/storage_attestation","attest_author":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK/action/author_attestation","sign_citation":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK/action/citation_signature","submit_replication":"https://pith.science/pith/BM6CHOITF6RYNDHJOPOPF2AHMK/action/replication_record"}},"created_at":"2026-07-04T23:55:13.220476+00:00","updated_at":"2026-07-04T23:55:13.220476+00:00"}