{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZS57S52O5BEKORJFUKV7HG5VSZ","short_pith_number":"pith:ZS57S52O","schema_version":"1.0","canonical_sha256":"ccbbf9774ee848a74525a2abf39bb5967efd0c9f24155eab902ec4d7996d74ca","source":{"kind":"arxiv","id":"2407.05540","version":1},"attestation_state":"computed","paper":{"title":"GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Wang, Chenxin Li, Jing Shao, Weihao Yu, Xinyu Liu, Yifan Liu, Yixuan Yuan","submitted_at":"2024-07-08T01:06:13Z","abstract_excerpt":"Recent advances in learning multi-modal representation have witnessed the success in biomedical domains. While established techniques enable handling multi-modal information, the challenges are posed when extended to various clinical modalities and practical modalitymissing setting due to the inherent modality gaps. To tackle these, we propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o), which embeds the numerous disparate clinical modalities into a unified representation, completes the deficient embedding of missing modality and reformulates the cross-m"},"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":"2407.05540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-08T01:06:13Z","cross_cats_sorted":[],"title_canon_sha256":"50acaa8cfabbb8ce993be791dc02d245fe8e2a2a9da1fd6111c033011b0bd3c5","abstract_canon_sha256":"d60765c66dbec4856d2192fde02b0e2751a2d3edba06fc1b96f1c3e265fde358"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:14.977875Z","signature_b64":"rVUcmfQaaZGePf3UaOvhjCQtuTQ97sq3yAS8dM84x5/VFLivQsLiUgfut0P0XHMUbPkJ5MLEDBLvlYES8VroBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccbbf9774ee848a74525a2abf39bb5967efd0c9f24155eab902ec4d7996d74ca","last_reissued_at":"2026-07-05T08:41:14.977466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:14.977466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Wang, Chenxin Li, Jing Shao, Weihao Yu, Xinyu Liu, Yifan Liu, Yixuan Yuan","submitted_at":"2024-07-08T01:06:13Z","abstract_excerpt":"Recent advances in learning multi-modal representation have witnessed the success in biomedical domains. While established techniques enable handling multi-modal information, the challenges are posed when extended to various clinical modalities and practical modalitymissing setting due to the inherent modality gaps. To tackle these, we propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o), which embeds the numerous disparate clinical modalities into a unified representation, completes the deficient embedding of missing modality and reformulates the cross-m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05540","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/2407.05540/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":"2407.05540","created_at":"2026-07-05T08:41:14.977528+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.05540v1","created_at":"2026-07-05T08:41:14.977528+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05540","created_at":"2026-07-05T08:41:14.977528+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZS57S52O5BEK","created_at":"2026-07-05T08:41:14.977528+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZS57S52O5BEKORJF","created_at":"2026-07-05T08:41:14.977528+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZS57S52O","created_at":"2026-07-05T08:41:14.977528+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/ZS57S52O5BEKORJFUKV7HG5VSZ","json":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ.json","graph_json":"https://pith.science/api/pith-number/ZS57S52O5BEKORJFUKV7HG5VSZ/graph.json","events_json":"https://pith.science/api/pith-number/ZS57S52O5BEKORJFUKV7HG5VSZ/events.json","paper":"https://pith.science/paper/ZS57S52O"},"agent_actions":{"view_html":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ","download_json":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ.json","view_paper":"https://pith.science/paper/ZS57S52O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.05540&json=true","fetch_graph":"https://pith.science/api/pith-number/ZS57S52O5BEKORJFUKV7HG5VSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZS57S52O5BEKORJFUKV7HG5VSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ/action/storage_attestation","attest_author":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ/action/author_attestation","sign_citation":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ/action/citation_signature","submit_replication":"https://pith.science/pith/ZS57S52O5BEKORJFUKV7HG5VSZ/action/replication_record"}},"created_at":"2026-07-05T08:41:14.977528+00:00","updated_at":"2026-07-05T08:41:14.977528+00:00"}