{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YA6H6HPN743ZLIDYJOIBMINV7L","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":"33c383acb162b0149b9218594c5377664838f61ada1eccdb9dcc4e7972f19994","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-07-24T13:39:21Z","title_canon_sha256":"c8307e650186d6183dd0ec457cc57dd9c0db954c5458b407ba2969b35e23d096"},"schema_version":"1.0","source":{"id":"2307.12790","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.12790","created_at":"2026-07-05T06:33:58Z"},{"alias_kind":"arxiv_version","alias_value":"2307.12790v1","created_at":"2026-07-05T06:33:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.12790","created_at":"2026-07-05T06:33:58Z"},{"alias_kind":"pith_short_12","alias_value":"YA6H6HPN743Z","created_at":"2026-07-05T06:33:58Z"},{"alias_kind":"pith_short_16","alias_value":"YA6H6HPN743ZLIDY","created_at":"2026-07-05T06:33:58Z"},{"alias_kind":"pith_short_8","alias_value":"YA6H6HPN","created_at":"2026-07-05T06:33:58Z"}],"graph_snapshots":[{"event_id":"sha256:a69c38d7f9f24cf06a951a70a365b4cb5c8339eb2aadf3a88fea55e85daa0e74","target":"graph","created_at":"2026-07-05T06:33:58Z","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/2307.12790/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have been employed across a diverse range of domains, encompassing drug discovery, protein interactions, semantic segmentation, and fluid dynamics research. In this study, we investigate the potential of Graph Neural Networks (GNNs) for medical image classification. We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedne","authors_text":"Aryan Singh, Ciar\\'an Eising, Patrick Denny, Pepijn Van de Ven","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-07-24T13:39:21Z","title":"Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.12790","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":"sha256:0f6960e5e3a7c962836f6f5bc4dc35f1407d089d1015b34dbfcc433337035705","target":"record","created_at":"2026-07-05T06:33:58Z","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":"33c383acb162b0149b9218594c5377664838f61ada1eccdb9dcc4e7972f19994","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-07-24T13:39:21Z","title_canon_sha256":"c8307e650186d6183dd0ec457cc57dd9c0db954c5458b407ba2969b35e23d096"},"schema_version":"1.0","source":{"id":"2307.12790","kind":"arxiv","version":1}},"canonical_sha256":"c03c7f1dedff3795a0784b901621b5fad5ce5e23d5d56d97d168d79861e68e11","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c03c7f1dedff3795a0784b901621b5fad5ce5e23d5d56d97d168d79861e68e11","first_computed_at":"2026-07-05T06:33:58.602976Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:33:58.602976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rH/S01m++PTtCdzUcOFulZ6Azj246X0AWYlW5wFyPa7ESO8G9/SINYjprXEShvfCOlG1KowIQA/wQCDEn0DOBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:33:58.603395Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.12790","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f6960e5e3a7c962836f6f5bc4dc35f1407d089d1015b34dbfcc433337035705","sha256:a69c38d7f9f24cf06a951a70a365b4cb5c8339eb2aadf3a88fea55e85daa0e74"],"state_sha256":"55e26b66d62ab6cd1cf3a476252f7d2ce30e651e20b5709163ffd87ddc9ad78e"}