{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YLE37W3N2S7SZS5ILGLQYG3H33","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":"ece666dee830d5ba61708ce07182bbd3df185c90bfd99c4969a1aaffe5a28dae","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-08T20:49:01Z","title_canon_sha256":"4230ee67e083cd21ab1e26112352f082a5eba1ace0be9a344171735ded37c93b"},"schema_version":"1.0","source":{"id":"2505.08798","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08798","created_at":"2026-07-05T11:02:44Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08798v1","created_at":"2026-07-05T11:02:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08798","created_at":"2026-07-05T11:02:44Z"},{"alias_kind":"pith_short_12","alias_value":"YLE37W3N2S7S","created_at":"2026-07-05T11:02:44Z"},{"alias_kind":"pith_short_16","alias_value":"YLE37W3N2S7SZS5I","created_at":"2026-07-05T11:02:44Z"},{"alias_kind":"pith_short_8","alias_value":"YLE37W3N","created_at":"2026-07-05T11:02:44Z"}],"graph_snapshots":[{"event_id":"sha256:3369243e37c2697997090810fa92aeba5de35efa5026d8bbbda91953cfb12bc1","target":"graph","created_at":"2026-07-05T11:02:44Z","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/2505.08798/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The application of AI in oncology has been limited by its reliance on large, annotated datasets and the need for retraining models for domain-specific diagnostic tasks. Taking heed of these limitations, we investigated in-context learning as a pragmatic alternative to model retraining by allowing models to adapt to new diagnostic tasks using only a few labeled examples at inference, without the need for retraining. Using four vision-language models (VLMs)-Paligemma, CLIP, ALIGN and GPT-4o, we evaluated the performance across three oncology datasets: MHIST, PatchCamelyon and HAM10000. To the be","authors_text":"Asis Shrestha, Bishwas Mandal, Mobina Shrestha, Vishal Mandal","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-08T20:49:01Z","title":"In-Context Learning for Label-Efficient Cancer Image Classification in Oncology"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08798","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":"sha256:98effcc32b2b20d770996c350d58135abb00a7cd73a9603334b7bd828ac4139d","target":"record","created_at":"2026-07-05T11:02:44Z","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":"ece666dee830d5ba61708ce07182bbd3df185c90bfd99c4969a1aaffe5a28dae","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-08T20:49:01Z","title_canon_sha256":"4230ee67e083cd21ab1e26112352f082a5eba1ace0be9a344171735ded37c93b"},"schema_version":"1.0","source":{"id":"2505.08798","kind":"arxiv","version":1}},"canonical_sha256":"c2c9bfdb6dd4bf2ccba859970c1b67deeaf7382d905b7a7f7661773d54e2d2c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2c9bfdb6dd4bf2ccba859970c1b67deeaf7382d905b7a7f7661773d54e2d2c8","first_computed_at":"2026-07-05T11:02:44.471012Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:44.471012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L8becMKr+u0c3ib/4VYbP8z4ZO80kiE1dtUMW+FlWdmDW0/JmpV7WqulCjei5XOMy+enCdoPeW0RIr0t/k8NAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:44.471496Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.08798","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98effcc32b2b20d770996c350d58135abb00a7cd73a9603334b7bd828ac4139d","sha256:3369243e37c2697997090810fa92aeba5de35efa5026d8bbbda91953cfb12bc1"],"state_sha256":"b2eab71fbd16140ac8efce1017aa60c4ca09c9769ab7a53b2f6dd74fac133c85"}