{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OQ3M2XIVEYP6TMZA36KXQBVEJI","short_pith_number":"pith:OQ3M2XIV","canonical_record":{"source":{"id":"2101.09976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T09:37:32Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"4ea0aaf30f2d3cf792affffd0213b6836fa0d9c2ba0203da452a37b1f8876d43","abstract_canon_sha256":"a4e10e7ff84a34113120f33eb91fbf2349dd6819992dcbf8e996b07a9610ede7"},"schema_version":"1.0"},"canonical_sha256":"7436cd5d15261fe9b320df957806a44a054e9e140a4aba2edf25ab735b85873d","source":{"kind":"arxiv","id":"2101.09976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.09976","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"arxiv_version","alias_value":"2101.09976v1","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.09976","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_12","alias_value":"OQ3M2XIVEYP6","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_16","alias_value":"OQ3M2XIVEYP6TMZA","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_8","alias_value":"OQ3M2XIV","created_at":"2026-07-05T02:09:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OQ3M2XIVEYP6TMZA36KXQBVEJI","target":"record","payload":{"canonical_record":{"source":{"id":"2101.09976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T09:37:32Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"4ea0aaf30f2d3cf792affffd0213b6836fa0d9c2ba0203da452a37b1f8876d43","abstract_canon_sha256":"a4e10e7ff84a34113120f33eb91fbf2349dd6819992dcbf8e996b07a9610ede7"},"schema_version":"1.0"},"canonical_sha256":"7436cd5d15261fe9b320df957806a44a054e9e140a4aba2edf25ab735b85873d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:09:24.434200Z","signature_b64":"TeYa3Kj9msd3IeQ4KwfacFrADjGdDhW4YiAgI7N63qKOU0b7HtWXna9H6ubGVmfIk2+nKCgp/G1kwPQd8GWtAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7436cd5d15261fe9b320df957806a44a054e9e140a4aba2edf25ab735b85873d","last_reissued_at":"2026-07-05T02:09:24.433844Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:09:24.433844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.09976","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":"2026-07-05T02:09:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HwWj94L/eM9I84a+aTKzePZxDvEj8ZTpk4fhj7P29OfkYBN9P33IpT6+uW1psJ7PUdAK2Dz2A6ZTL3rxMDXqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:38:36.378418Z"},"content_sha256":"6b4045641083e81e78c8bb882181cd1f533e3d0cc0a1c2c58f4c7051e5d39769","schema_version":"1.0","event_id":"sha256:6b4045641083e81e78c8bb882181cd1f533e3d0cc0a1c2c58f4c7051e5d39769"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OQ3M2XIVEYP6TMZA36KXQBVEJI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Bernd Hamm, Janis L. Vahldiek, Keno K. Bressem, Lisa C. Adams, Marcus R. Makowski, Stefan M. Niehues","submitted_at":"2021-01-25T09:37:32Z","abstract_excerpt":"Segmentation of pulmonary infiltrates can help assess severity of COVID-19, but manual segmentation is labor and time-intensive. Using neural networks to segment pulmonary infiltrates would enable automation of this task. However, training a 3D U-Net from computed tomography (CT) data is time- and resource-intensive. In this work, we therefore developed and tested a solution on how transfer learning can be used to train state-of-the-art segmentation models on limited hardware and in shorter time. We use the recently published RSNA International COVID-19 Open Radiology Database (RICORD) to trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.09976","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/2101.09976/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":"2026-07-05T02:09:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DLU4GRR1Lo+jaPxHgj5yuVdvbS+w0atp+FuAN9k19TW21oKctSAJ0pA8BjeK1v7WP/jIgfTfDxQC+cA/9mt4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:38:36.378956Z"},"content_sha256":"e91bd90117fdaa6e00387242b9d1feff4cac0c758493de522147b7e383f5f38f","schema_version":"1.0","event_id":"sha256:e91bd90117fdaa6e00387242b9d1feff4cac0c758493de522147b7e383f5f38f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/bundle.json","state_url":"https://pith.science/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/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":"2026-08-14T22:38:36Z","links":{"resolver":"https://pith.science/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI","bundle":"https://pith.science/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/bundle.json","state":"https://pith.science/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OQ3M2XIVEYP6TMZA36KXQBVEJI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OQ3M2XIVEYP6TMZA36KXQBVEJI","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":"a4e10e7ff84a34113120f33eb91fbf2349dd6819992dcbf8e996b07a9610ede7","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T09:37:32Z","title_canon_sha256":"4ea0aaf30f2d3cf792affffd0213b6836fa0d9c2ba0203da452a37b1f8876d43"},"schema_version":"1.0","source":{"id":"2101.09976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.09976","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"arxiv_version","alias_value":"2101.09976v1","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.09976","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_12","alias_value":"OQ3M2XIVEYP6","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_16","alias_value":"OQ3M2XIVEYP6TMZA","created_at":"2026-07-05T02:09:24Z"},{"alias_kind":"pith_short_8","alias_value":"OQ3M2XIV","created_at":"2026-07-05T02:09:24Z"}],"graph_snapshots":[{"event_id":"sha256:e91bd90117fdaa6e00387242b9d1feff4cac0c758493de522147b7e383f5f38f","target":"graph","created_at":"2026-07-05T02:09:24Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2101.09976/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Segmentation of pulmonary infiltrates can help assess severity of COVID-19, but manual segmentation is labor and time-intensive. Using neural networks to segment pulmonary infiltrates would enable automation of this task. However, training a 3D U-Net from computed tomography (CT) data is time- and resource-intensive. In this work, we therefore developed and tested a solution on how transfer learning can be used to train state-of-the-art segmentation models on limited hardware and in shorter time. We use the recently published RSNA International COVID-19 Open Radiology Database (RICORD) to trai","authors_text":"Bernd Hamm, Janis L. Vahldiek, Keno K. Bressem, Lisa C. Adams, Marcus R. Makowski, Stefan M. Niehues","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T09:37:32Z","title":"3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.09976","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":"sha256:6b4045641083e81e78c8bb882181cd1f533e3d0cc0a1c2c58f4c7051e5d39769","target":"record","created_at":"2026-07-05T02:09:24Z","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":"a4e10e7ff84a34113120f33eb91fbf2349dd6819992dcbf8e996b07a9610ede7","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T09:37:32Z","title_canon_sha256":"4ea0aaf30f2d3cf792affffd0213b6836fa0d9c2ba0203da452a37b1f8876d43"},"schema_version":"1.0","source":{"id":"2101.09976","kind":"arxiv","version":1}},"canonical_sha256":"7436cd5d15261fe9b320df957806a44a054e9e140a4aba2edf25ab735b85873d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7436cd5d15261fe9b320df957806a44a054e9e140a4aba2edf25ab735b85873d","first_computed_at":"2026-07-05T02:09:24.433844Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:09:24.433844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TeYa3Kj9msd3IeQ4KwfacFrADjGdDhW4YiAgI7N63qKOU0b7HtWXna9H6ubGVmfIk2+nKCgp/G1kwPQd8GWtAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:09:24.434200Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.09976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b4045641083e81e78c8bb882181cd1f533e3d0cc0a1c2c58f4c7051e5d39769","sha256:e91bd90117fdaa6e00387242b9d1feff4cac0c758493de522147b7e383f5f38f"],"state_sha256":"b0f09a5a5b3fd62cd36ada9120813f3483fcc5de37517764afc2e27968f4d3c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W734cKmNi1feAIJyo04q+EBxISlvpBzrgsP4d6lVWVApPcLr4WTreskl+qGJOEAXdh30cODnpk0HvyiBY2OADg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T22:38:36.384004Z","bundle_sha256":"abb1212aa87a4ecce077bc08d635668c40d7e8c98ead6d1c16612dd1b4420e33"}}