{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TMX3AMVOXGR2BWJ563UMYWTVZX","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":"54916431b22949e07db7f5e9f079d0dbb803bf496e9a3110d2e88fc8291c7a45","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T02:48:15Z","title_canon_sha256":"1fc31c2b4603452bb9f64a41eae7ef53c2ed8cc239a3905b61a5225f871ab92f"},"schema_version":"1.0","source":{"id":"2604.27343","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.27343","created_at":"2026-06-05T01:15:24Z"},{"alias_kind":"arxiv_version","alias_value":"2604.27343v2","created_at":"2026-06-05T01:15:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.27343","created_at":"2026-06-05T01:15:24Z"},{"alias_kind":"pith_short_12","alias_value":"TMX3AMVOXGR2","created_at":"2026-06-05T01:15:24Z"},{"alias_kind":"pith_short_16","alias_value":"TMX3AMVOXGR2BWJ5","created_at":"2026-06-05T01:15:24Z"},{"alias_kind":"pith_short_8","alias_value":"TMX3AMVO","created_at":"2026-06-05T01:15:24Z"}],"graph_snapshots":[{"event_id":"sha256:52c017e1b81f440acd6d24c79c2e3ad4da19006286d68abc02e2fdc42eea6bdd","target":"graph","created_at":"2026-06-05T01:15:24Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"The proposed JI-ADF method demonstrates strong and well-balanced performance across lesion categories on the MILK10k benchmark, improving sensitivity and Dice score while maintaining high specificity and good calibration."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the MILK10k benchmark faithfully represents real-world clinical acquisition conditions and severe class imbalance, and that the observed improvements arise from the joint-individual learning and adaptive fusion rather than dataset-specific tuning or implementation details."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"JI-ADF fuses three modalities with adaptive decision fusion and a multimodal attention module to achieve balanced, well-calibrated performance on the imbalanced MILK10k skin lesion benchmark."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"JI-ADF integrates joint-individual learning and adaptive decision fusion for improved multimodal skin lesion classification."}],"snapshot_sha256":"779a8483ed76c2dec6a0dea677f2a7a11df86439bf2dbe9881e8b80415df628e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T22:39:37.051068Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T19:22:50.496392Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.27343/integrity.json","findings":[],"snapshot_sha256":"26b5f34cb5e0b9e33b9cd8d84e6372fa4a6e70b9dfb809335149090736bb64ee","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Skin lesion classification is essential for early dermatological diagnosis, yet many existing computer-aided systems rely primarily on dermoscopic images and underutilize the multimodal evidence routinely available in clinical practice. To address this gap, we propose \\textbf{JI-ADF}, a trimodal deep learning framework that integrates dermoscopic images, clinical photographs, and structured patient metadata for clinically grounded skin lesion classification. The proposed architecture combines joint multimodal representation learning with modality-specific auxiliary supervision and an adaptive ","authors_text":"Dat Cao, Hien Chu, Hien Kha, Minh Le, Nguyen Quoc Khanh Le, Phan Nguyen, Trang Pham","cross_cats":[],"headline":"JI-ADF integrates joint-individual learning and adaptive decision fusion for improved multimodal skin lesion classification.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T02:48:15Z","title":"JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.27343","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-07T09:31:38.136986Z","id":"7cf67d6a-9250-44b5-afaa-29f9a1f58a76","model_set":{"reader":"grok-4.3"},"one_line_summary":"JI-ADF fuses three modalities with adaptive decision fusion and a multimodal attention module to achieve balanced, well-calibrated performance on the imbalanced MILK10k skin lesion benchmark.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"JI-ADF integrates joint-individual learning and adaptive decision fusion for improved multimodal skin lesion classification.","strongest_claim":"The proposed JI-ADF method demonstrates strong and well-balanced performance across lesion categories on the MILK10k benchmark, improving sensitivity and Dice score while maintaining high specificity and good calibration.","weakest_assumption":"That the MILK10k benchmark faithfully represents real-world clinical acquisition conditions and severe class imbalance, and that the observed improvements arise from the joint-individual learning and adaptive fusion rather than dataset-specific tuning or implementation details."}},"verdict_id":"7cf67d6a-9250-44b5-afaa-29f9a1f58a76"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c221dbad341977426aba45fa7d1ecba5c246500573337a4708e1e67f6d8bf94","target":"record","created_at":"2026-06-05T01:15:24Z","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":"54916431b22949e07db7f5e9f079d0dbb803bf496e9a3110d2e88fc8291c7a45","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T02:48:15Z","title_canon_sha256":"1fc31c2b4603452bb9f64a41eae7ef53c2ed8cc239a3905b61a5225f871ab92f"},"schema_version":"1.0","source":{"id":"2604.27343","kind":"arxiv","version":2}},"canonical_sha256":"9b2fb032aeb9a3a0d93df6e8cc5a75cdf9d05e736ae5230370a6caf1355a9f57","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b2fb032aeb9a3a0d93df6e8cc5a75cdf9d05e736ae5230370a6caf1355a9f57","first_computed_at":"2026-06-05T01:15:24.989914Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-05T01:15:24.989914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vCdyDx8+iEmpw7jde9T9e65nD84Cj2JuHITvPyI7p0U5N6D9CR0iawDzoRAlPnhI+p0VapJrxABiQUGiGUQqCA==","signature_status":"signed_v1","signed_at":"2026-06-05T01:15:24.990378Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.27343","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c221dbad341977426aba45fa7d1ecba5c246500573337a4708e1e67f6d8bf94","sha256:52c017e1b81f440acd6d24c79c2e3ad4da19006286d68abc02e2fdc42eea6bdd"],"state_sha256":"42a939dcf5942e5ce4953ec635f6ae9e5c9424e34aa458e748bd2ada7a76f51d"}