{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:J3JE3LSY647PVUNPXO3OJEGHJE","short_pith_number":"pith:J3JE3LSY","canonical_record":{"source":{"id":"2403.07407","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T08:34:34Z","cross_cats_sorted":[],"title_canon_sha256":"aa77af94dc3301de8e8dc676eb6cc6c948721c59d3b1ac05d67e5fb5795308c4","abstract_canon_sha256":"c920aa2e1cc28cea83fbc5f1ce136f3dc8c4e22b7a085bf367f77417275267d0"},"schema_version":"1.0"},"canonical_sha256":"4ed24dae58f73efad1afbbb6e490c749082e5d856429f1731b9509232383e9d9","source":{"kind":"arxiv","id":"2403.07407","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07407","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07407v1","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07407","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_12","alias_value":"J3JE3LSY647P","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_16","alias_value":"J3JE3LSY647PVUNP","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_8","alias_value":"J3JE3LSY","created_at":"2026-07-05T07:55:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:J3JE3LSY647PVUNPXO3OJEGHJE","target":"record","payload":{"canonical_record":{"source":{"id":"2403.07407","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T08:34:34Z","cross_cats_sorted":[],"title_canon_sha256":"aa77af94dc3301de8e8dc676eb6cc6c948721c59d3b1ac05d67e5fb5795308c4","abstract_canon_sha256":"c920aa2e1cc28cea83fbc5f1ce136f3dc8c4e22b7a085bf367f77417275267d0"},"schema_version":"1.0"},"canonical_sha256":"4ed24dae58f73efad1afbbb6e490c749082e5d856429f1731b9509232383e9d9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:03.182551Z","signature_b64":"HnhcPkasor60qXOxsmcwnhvr3DjvkHM/NrDLEq0opCLWIUR5t6X66IXFKz88Tj7QwLe0iZa9oGCybIe2P3poCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ed24dae58f73efad1afbbb6e490c749082e5d856429f1731b9509232383e9d9","last_reissued_at":"2026-07-05T07:55:03.182115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:03.182115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.07407","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:55:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T0J/eo81BRMUq1oOUPiZaQjORI/8Qb+usd2th2LTFyj6HFJIt8G6XjGqimowCaF1ebbPYaiDbE9fG9jvVZGmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:22:38.035311Z"},"content_sha256":"ac061735dafcec5db69c61d740c7d35d08e0be044eb828c2135fec066fcdb6c6","schema_version":"1.0","event_id":"sha256:ac061735dafcec5db69c61d740c7d35d08e0be044eb828c2135fec066fcdb6c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:J3JE3LSY647PVUNPXO3OJEGHJE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-context learning enables multimodal large language models to classify cancer pathology images","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Truhn, Dirk J\\\"ager, Dyke Ferber, Georg W\\\"olflein, Gustav M\\\"uller-Franzes, Isabella C. Wiest, Jakob Nikolas Kather, Marta Ligero, Narmin Ghaffari Laleh, Omar S.M. El Nahhas, Srividhya Sainath","submitted_at":"2024-03-12T08:34:34Z","abstract_excerpt":"Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates. Yet, in-context learning remains underexplored in medical image analysis. Here, we systematically evaluate the model Generative Pretrained Transformer 4 with Vision capabilities (GPT-4V) on cancer image processing with i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07407","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/2403.07407/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":"2026-07-05T07:55:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4WbQyhwMJcs9E/Bd5kcjhORmt6BrUgGlRMy/rZnSlyqfFaZ8fgrBqoJMMTqmX6E+nDTukfmSyk2UfridavFADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:22:38.035858Z"},"content_sha256":"31b9a2aa46c3e528b5bfd5545c63e02471290ccb8612d17e3f5abcb8f95347e5","schema_version":"1.0","event_id":"sha256:31b9a2aa46c3e528b5bfd5545c63e02471290ccb8612d17e3f5abcb8f95347e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3JE3LSY647PVUNPXO3OJEGHJE/bundle.json","state_url":"https://pith.science/pith/J3JE3LSY647PVUNPXO3OJEGHJE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3JE3LSY647PVUNPXO3OJEGHJE/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":"2026-08-10T16:22:38Z","links":{"resolver":"https://pith.science/pith/J3JE3LSY647PVUNPXO3OJEGHJE","bundle":"https://pith.science/pith/J3JE3LSY647PVUNPXO3OJEGHJE/bundle.json","state":"https://pith.science/pith/J3JE3LSY647PVUNPXO3OJEGHJE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3JE3LSY647PVUNPXO3OJEGHJE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:J3JE3LSY647PVUNPXO3OJEGHJE","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":"c920aa2e1cc28cea83fbc5f1ce136f3dc8c4e22b7a085bf367f77417275267d0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T08:34:34Z","title_canon_sha256":"aa77af94dc3301de8e8dc676eb6cc6c948721c59d3b1ac05d67e5fb5795308c4"},"schema_version":"1.0","source":{"id":"2403.07407","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07407","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07407v1","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07407","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_12","alias_value":"J3JE3LSY647P","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_16","alias_value":"J3JE3LSY647PVUNP","created_at":"2026-07-05T07:55:03Z"},{"alias_kind":"pith_short_8","alias_value":"J3JE3LSY","created_at":"2026-07-05T07:55:03Z"}],"graph_snapshots":[{"event_id":"sha256:31b9a2aa46c3e528b5bfd5545c63e02471290ccb8612d17e3f5abcb8f95347e5","target":"graph","created_at":"2026-07-05T07:55:03Z","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/2403.07407/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates. Yet, in-context learning remains underexplored in medical image analysis. Here, we systematically evaluate the model Generative Pretrained Transformer 4 with Vision capabilities (GPT-4V) on cancer image processing with i","authors_text":"Daniel Truhn, Dirk J\\\"ager, Dyke Ferber, Georg W\\\"olflein, Gustav M\\\"uller-Franzes, Isabella C. Wiest, Jakob Nikolas Kather, Marta Ligero, Narmin Ghaffari Laleh, Omar S.M. El Nahhas, Srividhya Sainath","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T08:34:34Z","title":"In-context learning enables multimodal large language models to classify cancer pathology images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07407","kind":"arxiv","version":1},"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:ac061735dafcec5db69c61d740c7d35d08e0be044eb828c2135fec066fcdb6c6","target":"record","created_at":"2026-07-05T07:55:03Z","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":"c920aa2e1cc28cea83fbc5f1ce136f3dc8c4e22b7a085bf367f77417275267d0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T08:34:34Z","title_canon_sha256":"aa77af94dc3301de8e8dc676eb6cc6c948721c59d3b1ac05d67e5fb5795308c4"},"schema_version":"1.0","source":{"id":"2403.07407","kind":"arxiv","version":1}},"canonical_sha256":"4ed24dae58f73efad1afbbb6e490c749082e5d856429f1731b9509232383e9d9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ed24dae58f73efad1afbbb6e490c749082e5d856429f1731b9509232383e9d9","first_computed_at":"2026-07-05T07:55:03.182115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:55:03.182115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HnhcPkasor60qXOxsmcwnhvr3DjvkHM/NrDLEq0opCLWIUR5t6X66IXFKz88Tj7QwLe0iZa9oGCybIe2P3poCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:55:03.182551Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.07407","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac061735dafcec5db69c61d740c7d35d08e0be044eb828c2135fec066fcdb6c6","sha256:31b9a2aa46c3e528b5bfd5545c63e02471290ccb8612d17e3f5abcb8f95347e5"],"state_sha256":"cbac2707d317d23d41265aaf71e44f4d0a894cfdf1d949afda717ecb58686726"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4xVnUaa2rDmDU803ZrNysjudBmA8TnooJZ/6NqLSLYyqqEEoQMfnQS0djlSMe/PKyGbHdvc/vchYyjk4leH2Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T16:22:38.040129Z","bundle_sha256":"9c0b4a21194aa7b1a541ea7034273239877e6672c08251ab741df9de3b584d90"}}