{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JLH722MH3SI5JHCPZQUDYS2YTQ","short_pith_number":"pith:JLH722MH","canonical_record":{"source":{"id":"2412.05888","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-08T10:50:59Z","cross_cats_sorted":[],"title_canon_sha256":"486d243fe9842f0f86474c41c107ba5c99166edc2c97d766544a905b06170eb9","abstract_canon_sha256":"0c591162e0859a5f8ee105af25388bd89f5a4597eb60c452dc93108eae49981f"},"schema_version":"1.0"},"canonical_sha256":"4acffd6987dc91d49c4fcc283c4b589c06cf1c7f410e50badad017de26863f2c","source":{"kind":"arxiv","id":"2412.05888","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05888","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05888v3","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05888","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_12","alias_value":"JLH722MH3SI5","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_16","alias_value":"JLH722MH3SI5JHCP","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_8","alias_value":"JLH722MH","created_at":"2026-07-05T11:40:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JLH722MH3SI5JHCPZQUDYS2YTQ","target":"record","payload":{"canonical_record":{"source":{"id":"2412.05888","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-08T10:50:59Z","cross_cats_sorted":[],"title_canon_sha256":"486d243fe9842f0f86474c41c107ba5c99166edc2c97d766544a905b06170eb9","abstract_canon_sha256":"0c591162e0859a5f8ee105af25388bd89f5a4597eb60c452dc93108eae49981f"},"schema_version":"1.0"},"canonical_sha256":"4acffd6987dc91d49c4fcc283c4b589c06cf1c7f410e50badad017de26863f2c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:51.238702Z","signature_b64":"PMXQsiEpxDnjCssSgtSfWWnZWF057zC4MNWAmipvyovU0ofmalpEurOKT6zoV6OihOdB3ss7YM5rJXOiZ/snCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4acffd6987dc91d49c4fcc283c4b589c06cf1c7f410e50badad017de26863f2c","last_reissued_at":"2026-07-05T11:40:51.238114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:51.238114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.05888","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:40:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m7tQ/pnOuWYCPimuiMp7qyAroXuubCDaUAXWk4Iz+X+scrGfugBVsf1l0cDUmnBhO4uwhzRJladN48Qlck5pDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:59:00.186958Z"},"content_sha256":"2cadb401031b4699d273ea3b67f5e4a2a781cdab2ef8bfe6a35263a7301ce88d","schema_version":"1.0","event_id":"sha256:2cadb401031b4699d273ea3b67f5e4a2a781cdab2ef8bfe6a35263a7301ce88d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JLH722MH3SI5JHCPZQUDYS2YTQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donghang Lyu, Marius Staring, Ruochen Gao","submitted_at":"2024-12-08T10:50:59Z","abstract_excerpt":"Medical image segmentation involves partitioning medical images into meaningful regions, with a focus on identifying anatomical structures and lesions. It has broad applications in healthcare, and deep learning methods have enabled significant advancements in automating this process. Recently, the introduction of the Segmentation Anything Model (SAM), the first foundation model for segmentation task, has prompted researchers to adapt it for the medical domain to improve performance across various tasks. However, SAM's large model size and high GPU requirements hinder its scalability and develo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05888","kind":"arxiv","version":3},"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/2412.05888/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:40:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AFsjnkZKmEKpzGaroEzyKN8Olb+Nkex6BLrFsxW4AtrR2PF0Z1LbfId/hkfIPVf2V/Gg4mOjISoIAnCSJExzBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:59:00.187345Z"},"content_sha256":"aa9ac9b2b793991088702fca9a8f1e8228404131a760d16c4afce25f63afafc0","schema_version":"1.0","event_id":"sha256:aa9ac9b2b793991088702fca9a8f1e8228404131a760d16c4afce25f63afafc0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/bundle.json","state_url":"https://pith.science/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T15:59:00Z","links":{"resolver":"https://pith.science/pith/JLH722MH3SI5JHCPZQUDYS2YTQ","bundle":"https://pith.science/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/bundle.json","state":"https://pith.science/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JLH722MH3SI5JHCPZQUDYS2YTQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JLH722MH3SI5JHCPZQUDYS2YTQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0c591162e0859a5f8ee105af25388bd89f5a4597eb60c452dc93108eae49981f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-08T10:50:59Z","title_canon_sha256":"486d243fe9842f0f86474c41c107ba5c99166edc2c97d766544a905b06170eb9"},"schema_version":"1.0","source":{"id":"2412.05888","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05888","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05888v3","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05888","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_12","alias_value":"JLH722MH3SI5","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_16","alias_value":"JLH722MH3SI5JHCP","created_at":"2026-07-05T11:40:51Z"},{"alias_kind":"pith_short_8","alias_value":"JLH722MH","created_at":"2026-07-05T11:40:51Z"}],"graph_snapshots":[{"event_id":"sha256:aa9ac9b2b793991088702fca9a8f1e8228404131a760d16c4afce25f63afafc0","target":"graph","created_at":"2026-07-05T11:40:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.05888/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation involves partitioning medical images into meaningful regions, with a focus on identifying anatomical structures and lesions. It has broad applications in healthcare, and deep learning methods have enabled significant advancements in automating this process. Recently, the introduction of the Segmentation Anything Model (SAM), the first foundation model for segmentation task, has prompted researchers to adapt it for the medical domain to improve performance across various tasks. However, SAM's large model size and high GPU requirements hinder its scalability and develo","authors_text":"Donghang Lyu, Marius Staring, Ruochen Gao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-08T10:50:59Z","title":"MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05888","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2cadb401031b4699d273ea3b67f5e4a2a781cdab2ef8bfe6a35263a7301ce88d","target":"record","created_at":"2026-07-05T11:40:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0c591162e0859a5f8ee105af25388bd89f5a4597eb60c452dc93108eae49981f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-08T10:50:59Z","title_canon_sha256":"486d243fe9842f0f86474c41c107ba5c99166edc2c97d766544a905b06170eb9"},"schema_version":"1.0","source":{"id":"2412.05888","kind":"arxiv","version":3}},"canonical_sha256":"4acffd6987dc91d49c4fcc283c4b589c06cf1c7f410e50badad017de26863f2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4acffd6987dc91d49c4fcc283c4b589c06cf1c7f410e50badad017de26863f2c","first_computed_at":"2026-07-05T11:40:51.238114Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:40:51.238114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PMXQsiEpxDnjCssSgtSfWWnZWF057zC4MNWAmipvyovU0ofmalpEurOKT6zoV6OihOdB3ss7YM5rJXOiZ/snCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:40:51.238702Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.05888","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2cadb401031b4699d273ea3b67f5e4a2a781cdab2ef8bfe6a35263a7301ce88d","sha256:aa9ac9b2b793991088702fca9a8f1e8228404131a760d16c4afce25f63afafc0"],"state_sha256":"6c53f1b74ce99d7cfbef45ca7e011d39eebe52999ffbda9ac2def03e78e2b07d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CH9XJ4JKvx0xEpGwbqGuNDEukrZl8/YF2i8kiii11NyhuNQlVIyarESK+N2lGZXxnteCzcx5ddzCTznHC9kwAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:59:00.189512Z","bundle_sha256":"a5a0ccd9b1ae0d5c611ce4fdf98a5ce9342fa3d3e437c4c9ffcd45fc944462bd"}}