{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VWMWIWEDOIG3BOIK7BKYD6SWV6","short_pith_number":"pith:VWMWIWED","schema_version":"1.0","canonical_sha256":"ad99645883720db0b90af85581fa56afb78e665a57d59d18c52fd128d40a0511","source":{"kind":"arxiv","id":"2210.15075","version":2},"attestation_state":"computed","paper":{"title":"IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hritam Basak, Rammohan Mallipeddi, Rohit Kundu, Sayan Nag, Soumitri Chattopadhyay","submitted_at":"2022-10-26T23:11:02Z","abstract_excerpt":"Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutiona"},"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":"2210.15075","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T23:11:02Z","cross_cats_sorted":[],"title_canon_sha256":"939a02558b04e06b5736a0e5b25dae3906f1301769d7926bd2eeabf9991bdaf4","abstract_canon_sha256":"440d4d63c6bba992e67f5013a34aa5b44ae8d2e3f7091f36ec8cb1478530b31d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:18.652923Z","signature_b64":"6KPwvAr0uEN67yLudh53fX6oPfBSIPkAWJoWe8jS/tuIVBHCfuknYD3pQ3ad1Y9qFOGUn0FzIMIphxAdOQciAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad99645883720db0b90af85581fa56afb78e665a57d59d18c52fd128d40a0511","last_reissued_at":"2026-07-05T05:47:18.652383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:18.652383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hritam Basak, Rammohan Mallipeddi, Rohit Kundu, Sayan Nag, Soumitri Chattopadhyay","submitted_at":"2022-10-26T23:11:02Z","abstract_excerpt":"Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutiona"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15075","kind":"arxiv","version":2},"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/2210.15075/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":"2210.15075","created_at":"2026-07-05T05:47:18.652440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.15075v2","created_at":"2026-07-05T05:47:18.652440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15075","created_at":"2026-07-05T05:47:18.652440+00:00"},{"alias_kind":"pith_short_12","alias_value":"VWMWIWEDOIG3","created_at":"2026-07-05T05:47:18.652440+00:00"},{"alias_kind":"pith_short_16","alias_value":"VWMWIWEDOIG3BOIK","created_at":"2026-07-05T05:47:18.652440+00:00"},{"alias_kind":"pith_short_8","alias_value":"VWMWIWED","created_at":"2026-07-05T05:47:18.652440+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/VWMWIWEDOIG3BOIK7BKYD6SWV6","json":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6.json","graph_json":"https://pith.science/api/pith-number/VWMWIWEDOIG3BOIK7BKYD6SWV6/graph.json","events_json":"https://pith.science/api/pith-number/VWMWIWEDOIG3BOIK7BKYD6SWV6/events.json","paper":"https://pith.science/paper/VWMWIWED"},"agent_actions":{"view_html":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6","download_json":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6.json","view_paper":"https://pith.science/paper/VWMWIWED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.15075&json=true","fetch_graph":"https://pith.science/api/pith-number/VWMWIWEDOIG3BOIK7BKYD6SWV6/graph.json","fetch_events":"https://pith.science/api/pith-number/VWMWIWEDOIG3BOIK7BKYD6SWV6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6/action/storage_attestation","attest_author":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6/action/author_attestation","sign_citation":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6/action/citation_signature","submit_replication":"https://pith.science/pith/VWMWIWEDOIG3BOIK7BKYD6SWV6/action/replication_record"}},"created_at":"2026-07-05T05:47:18.652440+00:00","updated_at":"2026-07-05T05:47:18.652440+00:00"}