{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P6BTUWRTVXCAGIKVVCFOKZX5WP","short_pith_number":"pith:P6BTUWRT","schema_version":"1.0","canonical_sha256":"7f833a5a33adc4032155a88ae566fdb3d643b63ba03d47e46cacb929010d6129","source":{"kind":"arxiv","id":"2311.10319","version":6},"attestation_state":"computed","paper":{"title":"Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Craig Smuda, Jacopo Cirrone, Jinqian Pan, Luoyao Chen, Mei Chen, Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari","submitted_at":"2023-11-17T04:04:29Z","abstract_excerpt":"Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine "},"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":"2311.10319","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T04:04:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"680cae53afc09e3cd7e692c79d66840f5569348fda6b11a1be53a31568cd5c27","abstract_canon_sha256":"6b8073a21e15685fe7670c7ad31e806a20664b9760e8163b04c0671bddc3eb6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:08.719431Z","signature_b64":"O21Df1urDLjjyTrSazn1USh5j9xR7SlYnN3UDCZhFAYSr42hI4tgnivRuyo3n94NVl7Wyg98W/hjjLsNL6MFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f833a5a33adc4032155a88ae566fdb3d643b63ba03d47e46cacb929010d6129","last_reissued_at":"2026-07-05T08:20:08.718955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:08.718955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Craig Smuda, Jacopo Cirrone, Jinqian Pan, Luoyao Chen, Mei Chen, Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari","submitted_at":"2023-11-17T04:04:29Z","abstract_excerpt":"Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10319","kind":"arxiv","version":6},"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/2311.10319/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":"2311.10319","created_at":"2026-07-05T08:20:08.719005+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.10319v6","created_at":"2026-07-05T08:20:08.719005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10319","created_at":"2026-07-05T08:20:08.719005+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6BTUWRTVXCA","created_at":"2026-07-05T08:20:08.719005+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6BTUWRTVXCAGIKV","created_at":"2026-07-05T08:20:08.719005+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6BTUWRT","created_at":"2026-07-05T08:20:08.719005+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/P6BTUWRTVXCAGIKVVCFOKZX5WP","json":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP.json","graph_json":"https://pith.science/api/pith-number/P6BTUWRTVXCAGIKVVCFOKZX5WP/graph.json","events_json":"https://pith.science/api/pith-number/P6BTUWRTVXCAGIKVVCFOKZX5WP/events.json","paper":"https://pith.science/paper/P6BTUWRT"},"agent_actions":{"view_html":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP","download_json":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP.json","view_paper":"https://pith.science/paper/P6BTUWRT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.10319&json=true","fetch_graph":"https://pith.science/api/pith-number/P6BTUWRTVXCAGIKVVCFOKZX5WP/graph.json","fetch_events":"https://pith.science/api/pith-number/P6BTUWRTVXCAGIKVVCFOKZX5WP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP/action/storage_attestation","attest_author":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP/action/author_attestation","sign_citation":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP/action/citation_signature","submit_replication":"https://pith.science/pith/P6BTUWRTVXCAGIKVVCFOKZX5WP/action/replication_record"}},"created_at":"2026-07-05T08:20:08.719005+00:00","updated_at":"2026-07-05T08:20:08.719005+00:00"}