{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:K5PRRM465H3WVT2L3KUU2G67TH","short_pith_number":"pith:K5PRRM46","schema_version":"1.0","canonical_sha256":"575f18b39ee9f76acf4bdaa94d1bdf99c9f1bffe93bebb21d4fa74e5fdb39e74","source":{"kind":"arxiv","id":"2308.12231","version":1},"attestation_state":"computed","paper":{"title":"SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Haoran Chen, Qing Xu, Wenting Duan, Wenwei Kuang, Xueyao Bao, Zeyu Zhang","submitted_at":"2023-08-23T16:13:58Z","abstract_excerpt":"Image segmentation plays an essential role in nuclei image analysis. Recently, the segment anything model has made a significant breakthrough in such tasks. However, the current model exists two major issues for cell segmentation: (1) the image encoder of the segment anything model involves a large number of parameters. Retraining or even fine-tuning the model still requires expensive computational resources. (2) in point prompt mode, points are sampled from the center of the ground truth and more than one set of points is expected to achieve reliable performance, which is not efficient for pr"},"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":"2308.12231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-08-23T16:13:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"24d2e12939fe2c8544c5cb69c5ddf86e0d6538b655f70c3822c4da480d4f2ef5","abstract_canon_sha256":"23bcc8b51f67766e21a22ff6e635c04b6f7f9e0ada7a3b1aac128c559cf5a440"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:01.707808Z","signature_b64":"HfTy+mv9hB0OnSrOCgCrGbpxITeoz5wge0xL9QHdLC8zlOs6i/BQDTaypE+DNb0pvvCxjWZhhtOKCpxcHgtrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"575f18b39ee9f76acf4bdaa94d1bdf99c9f1bffe93bebb21d4fa74e5fdb39e74","last_reissued_at":"2026-07-05T06:44:01.707383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:01.707383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Haoran Chen, Qing Xu, Wenting Duan, Wenwei Kuang, Xueyao Bao, Zeyu Zhang","submitted_at":"2023-08-23T16:13:58Z","abstract_excerpt":"Image segmentation plays an essential role in nuclei image analysis. Recently, the segment anything model has made a significant breakthrough in such tasks. However, the current model exists two major issues for cell segmentation: (1) the image encoder of the segment anything model involves a large number of parameters. Retraining or even fine-tuning the model still requires expensive computational resources. (2) in point prompt mode, points are sampled from the center of the ground truth and more than one set of points is expected to achieve reliable performance, which is not efficient for pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12231","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/2308.12231/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":"2308.12231","created_at":"2026-07-05T06:44:01.707442+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.12231v1","created_at":"2026-07-05T06:44:01.707442+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12231","created_at":"2026-07-05T06:44:01.707442+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5PRRM465H3W","created_at":"2026-07-05T06:44:01.707442+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5PRRM465H3WVT2L","created_at":"2026-07-05T06:44:01.707442+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5PRRM46","created_at":"2026-07-05T06:44:01.707442+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09562","citing_title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","ref_index":75,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH","json":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH.json","graph_json":"https://pith.science/api/pith-number/K5PRRM465H3WVT2L3KUU2G67TH/graph.json","events_json":"https://pith.science/api/pith-number/K5PRRM465H3WVT2L3KUU2G67TH/events.json","paper":"https://pith.science/paper/K5PRRM46"},"agent_actions":{"view_html":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH","download_json":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH.json","view_paper":"https://pith.science/paper/K5PRRM46","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.12231&json=true","fetch_graph":"https://pith.science/api/pith-number/K5PRRM465H3WVT2L3KUU2G67TH/graph.json","fetch_events":"https://pith.science/api/pith-number/K5PRRM465H3WVT2L3KUU2G67TH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH/action/storage_attestation","attest_author":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH/action/author_attestation","sign_citation":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH/action/citation_signature","submit_replication":"https://pith.science/pith/K5PRRM465H3WVT2L3KUU2G67TH/action/replication_record"}},"created_at":"2026-07-05T06:44:01.707442+00:00","updated_at":"2026-07-05T06:44:01.707442+00:00"}