{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BZCPJ3FHIKZC5PC66PZWTWJDCW","short_pith_number":"pith:BZCPJ3FH","canonical_record":{"source":{"id":"2410.14524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-18T15:08:05Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f09612ccd031fc118f4acb95e596bce82bbbbff890fa850953eda6edffa13349","abstract_canon_sha256":"8b15d9f76ad756e934551bd9807a0172e2b6956425298fc843ad194e5711ee3d"},"schema_version":"1.0"},"canonical_sha256":"0e44f4eca742b22ebc5ef3f369d923159acad72a33a822c0dfbacad61b396bbe","source":{"kind":"arxiv","id":"2410.14524","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.14524","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"arxiv_version","alias_value":"2410.14524v1","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14524","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_12","alias_value":"BZCPJ3FHIKZC","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_16","alias_value":"BZCPJ3FHIKZC5PC6","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_8","alias_value":"BZCPJ3FH","created_at":"2026-07-05T09:22:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BZCPJ3FHIKZC5PC66PZWTWJDCW","target":"record","payload":{"canonical_record":{"source":{"id":"2410.14524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-18T15:08:05Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f09612ccd031fc118f4acb95e596bce82bbbbff890fa850953eda6edffa13349","abstract_canon_sha256":"8b15d9f76ad756e934551bd9807a0172e2b6956425298fc843ad194e5711ee3d"},"schema_version":"1.0"},"canonical_sha256":"0e44f4eca742b22ebc5ef3f369d923159acad72a33a822c0dfbacad61b396bbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:33.583148Z","signature_b64":"uw/PDxA/EF9hk6lA4i4CGkbzJ/V5gXUMTDpYRQj3nHbYBCJLqJ2lnDrQRgbZAdOx8EYJ3d/7L597hoZEUZP1CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e44f4eca742b22ebc5ef3f369d923159acad72a33a822c0dfbacad61b396bbe","last_reissued_at":"2026-07-05T09:22:33.582692Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:33.582692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.14524","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:22:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VHcbkOFNM1e6+Mobzpst8vBm+XvXGePdWnnp7iIh0f602tC8xxib5L6jEMbUnti++ih+juHJoei6CkbkZwTYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:23:18.341544Z"},"content_sha256":"5dc4821ed328fc873e2a0e1b977f3ccb3d4d709735c01905dabe4ce1db874cb1","schema_version":"1.0","event_id":"sha256:5dc4821ed328fc873e2a0e1b977f3ccb3d4d709735c01905dabe4ce1db874cb1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BZCPJ3FHIKZC5PC66PZWTWJDCW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniel Wolf, Meinrad Beer, Michael G\\\"otz, Timo Ropinski, Tristan Payer","submitted_at":"2024-10-18T15:08:05Z","abstract_excerpt":"Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images. We hypothesize that the similarity of medical images hinders the success of contrastive learning in the medical imaging domain. To this end, we investigate different strategies based on deep embedding, information theory, and hashing in order to identify and reduce redundancy in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14524","kind":"arxiv","version":1},"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/2410.14524/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:22:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZgMOUJryU5Ur/XN1woNUCiR5vkRg3J/U8Z+f/BPgZE+rVVV3k72Xx774p4j0DTGvDn+KS/rbAmO7MliNGLdMDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:23:18.341927Z"},"content_sha256":"973d363fea4110558b16c214f410220cdd302dd877a0746df65f6524193e4508","schema_version":"1.0","event_id":"sha256:973d363fea4110558b16c214f410220cdd302dd877a0746df65f6524193e4508"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/bundle.json","state_url":"https://pith.science/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T12:23:18Z","links":{"resolver":"https://pith.science/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW","bundle":"https://pith.science/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/bundle.json","state":"https://pith.science/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BZCPJ3FHIKZC5PC66PZWTWJDCW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BZCPJ3FHIKZC5PC66PZWTWJDCW","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":"8b15d9f76ad756e934551bd9807a0172e2b6956425298fc843ad194e5711ee3d","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-18T15:08:05Z","title_canon_sha256":"f09612ccd031fc118f4acb95e596bce82bbbbff890fa850953eda6edffa13349"},"schema_version":"1.0","source":{"id":"2410.14524","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.14524","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"arxiv_version","alias_value":"2410.14524v1","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14524","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_12","alias_value":"BZCPJ3FHIKZC","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_16","alias_value":"BZCPJ3FHIKZC5PC6","created_at":"2026-07-05T09:22:33Z"},{"alias_kind":"pith_short_8","alias_value":"BZCPJ3FH","created_at":"2026-07-05T09:22:33Z"}],"graph_snapshots":[{"event_id":"sha256:973d363fea4110558b16c214f410220cdd302dd877a0746df65f6524193e4508","target":"graph","created_at":"2026-07-05T09:22:33Z","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/2410.14524/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images. We hypothesize that the similarity of medical images hinders the success of contrastive learning in the medical imaging domain. To this end, we investigate different strategies based on deep embedding, information theory, and hashing in order to identify and reduce redundancy in ","authors_text":"Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniel Wolf, Meinrad Beer, Michael G\\\"otz, Timo Ropinski, Tristan Payer","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-18T15:08:05Z","title":"Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14524","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":"sha256:5dc4821ed328fc873e2a0e1b977f3ccb3d4d709735c01905dabe4ce1db874cb1","target":"record","created_at":"2026-07-05T09:22:33Z","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":"8b15d9f76ad756e934551bd9807a0172e2b6956425298fc843ad194e5711ee3d","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-18T15:08:05Z","title_canon_sha256":"f09612ccd031fc118f4acb95e596bce82bbbbff890fa850953eda6edffa13349"},"schema_version":"1.0","source":{"id":"2410.14524","kind":"arxiv","version":1}},"canonical_sha256":"0e44f4eca742b22ebc5ef3f369d923159acad72a33a822c0dfbacad61b396bbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e44f4eca742b22ebc5ef3f369d923159acad72a33a822c0dfbacad61b396bbe","first_computed_at":"2026-07-05T09:22:33.582692Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:22:33.582692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uw/PDxA/EF9hk6lA4i4CGkbzJ/V5gXUMTDpYRQj3nHbYBCJLqJ2lnDrQRgbZAdOx8EYJ3d/7L597hoZEUZP1CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:22:33.583148Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.14524","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5dc4821ed328fc873e2a0e1b977f3ccb3d4d709735c01905dabe4ce1db874cb1","sha256:973d363fea4110558b16c214f410220cdd302dd877a0746df65f6524193e4508"],"state_sha256":"1e8f93c27ee673bf3f8abb9487fba39d26029fbaeeb55b4a161fcea931545c4b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CJyfUU6DmriACAK1SXehhnE99x0RRjf4gStxmJ0XHyPc2f0aCNNxLbDIgId3M5asmcmduNZ9p53YG/vhZ5JSDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T12:23:18.345720Z","bundle_sha256":"58c694404f4a799f491417e6517e36a94b1f3690f8d5f9fd8a7cc1600a150e71"}}