{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BW3YOUEUI2Y5D6FN6T2NGVL5AJ","short_pith_number":"pith:BW3YOUEU","canonical_record":{"source":{"id":"1910.02717","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-07T10:51:56Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5dcd318994400a18dcf2d00cb28a75059245ea41d1dbc5ebdfff8717461723c9","abstract_canon_sha256":"7777220c7aba4bfc745e56552db9d4e54e3c6733ff679a07f9f21f7f28d5f4c8"},"schema_version":"1.0"},"canonical_sha256":"0db787509446b1d1f8adf4f4d3557d027e020fd17a1a6e70bb1ef1e1b6c55ddd","source":{"kind":"arxiv","id":"1910.02717","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02717","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02717v2","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02717","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_12","alias_value":"BW3YOUEUI2Y5","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_16","alias_value":"BW3YOUEUI2Y5D6FN","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_8","alias_value":"BW3YOUEU","created_at":"2026-07-05T01:39:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BW3YOUEUI2Y5D6FN6T2NGVL5AJ","target":"record","payload":{"canonical_record":{"source":{"id":"1910.02717","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-07T10:51:56Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5dcd318994400a18dcf2d00cb28a75059245ea41d1dbc5ebdfff8717461723c9","abstract_canon_sha256":"7777220c7aba4bfc745e56552db9d4e54e3c6733ff679a07f9f21f7f28d5f4c8"},"schema_version":"1.0"},"canonical_sha256":"0db787509446b1d1f8adf4f4d3557d027e020fd17a1a6e70bb1ef1e1b6c55ddd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:39:24.873484Z","signature_b64":"M5jhAwdQuZLC7DCbz9638s5NnnTwozdmVPsB/HpPILGwYi6BG53IXWPAgq0HWQEu6E+suQD+FGz1sw6SoMm7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0db787509446b1d1f8adf4f4d3557d027e020fd17a1a6e70bb1ef1e1b6c55ddd","last_reissued_at":"2026-07-05T01:39:24.873102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:39:24.873102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.02717","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:39:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+UglN2Khe0apNe6ZsWZ94Ao/YooTncWE+GDrx4ejlZipU0Vwr/3/hg4d+EzzZsJsYmCa5SWDl2stjKpKyRGdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T06:54:06.100744Z"},"content_sha256":"3b039fe94a0a667dd43d8c8e4974ff297028519777ef8a34803e006ac7d99e23","schema_version":"1.0","event_id":"sha256:3b039fe94a0a667dd43d8c8e4974ff297028519777ef8a34803e006ac7d99e23"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BW3YOUEUI2Y5D6FN6T2NGVL5AJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Brain MRI Tumor Segmentation with Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Daniele Loiacono, Edoardo Giacomello, Luca Mainardi","submitted_at":"2019-10-07T10:51:56Z","abstract_excerpt":"Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks. In particular, we extend SegAN, successfully applied to the same task in a previous work, in two respects: (i) we used a different model input and (ii) we employed a modified loss function to train the model. We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS). First, we trained a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02717","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/1910.02717/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":"2026-07-05T01:39:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fvwjr/KpUN9AaDbkqzchyLhptNLwp27b0uitdJdKsa1S8P0ELuEogVC/PiEqTxgBzpN+MfvSKe6GsvyNrivxAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T06:54:06.101121Z"},"content_sha256":"24d3411434009ab7e455cf9c7f464c44aae0de966c71cea2ae5a3120be0df894","schema_version":"1.0","event_id":"sha256:24d3411434009ab7e455cf9c7f464c44aae0de966c71cea2ae5a3120be0df894"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/bundle.json","state_url":"https://pith.science/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/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":"2026-07-12T06:54:06Z","links":{"resolver":"https://pith.science/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ","bundle":"https://pith.science/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/bundle.json","state":"https://pith.science/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BW3YOUEUI2Y5D6FN6T2NGVL5AJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BW3YOUEUI2Y5D6FN6T2NGVL5AJ","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":"7777220c7aba4bfc745e56552db9d4e54e3c6733ff679a07f9f21f7f28d5f4c8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-07T10:51:56Z","title_canon_sha256":"5dcd318994400a18dcf2d00cb28a75059245ea41d1dbc5ebdfff8717461723c9"},"schema_version":"1.0","source":{"id":"1910.02717","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02717","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02717v2","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02717","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_12","alias_value":"BW3YOUEUI2Y5","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_16","alias_value":"BW3YOUEUI2Y5D6FN","created_at":"2026-07-05T01:39:24Z"},{"alias_kind":"pith_short_8","alias_value":"BW3YOUEU","created_at":"2026-07-05T01:39:24Z"}],"graph_snapshots":[{"event_id":"sha256:24d3411434009ab7e455cf9c7f464c44aae0de966c71cea2ae5a3120be0df894","target":"graph","created_at":"2026-07-05T01:39:24Z","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/1910.02717/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks. In particular, we extend SegAN, successfully applied to the same task in a previous work, in two respects: (i) we used a different model input and (ii) we employed a modified loss function to train the model. We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS). First, we trained a","authors_text":"Daniele Loiacono, Edoardo Giacomello, Luca Mainardi","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-07T10:51:56Z","title":"Brain MRI Tumor Segmentation with Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02717","kind":"arxiv","version":2},"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":"sha256:3b039fe94a0a667dd43d8c8e4974ff297028519777ef8a34803e006ac7d99e23","target":"record","created_at":"2026-07-05T01:39:24Z","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":"7777220c7aba4bfc745e56552db9d4e54e3c6733ff679a07f9f21f7f28d5f4c8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-07T10:51:56Z","title_canon_sha256":"5dcd318994400a18dcf2d00cb28a75059245ea41d1dbc5ebdfff8717461723c9"},"schema_version":"1.0","source":{"id":"1910.02717","kind":"arxiv","version":2}},"canonical_sha256":"0db787509446b1d1f8adf4f4d3557d027e020fd17a1a6e70bb1ef1e1b6c55ddd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0db787509446b1d1f8adf4f4d3557d027e020fd17a1a6e70bb1ef1e1b6c55ddd","first_computed_at":"2026-07-05T01:39:24.873102Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:39:24.873102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M5jhAwdQuZLC7DCbz9638s5NnnTwozdmVPsB/HpPILGwYi6BG53IXWPAgq0HWQEu6E+suQD+FGz1sw6SoMm7CA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:39:24.873484Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02717","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b039fe94a0a667dd43d8c8e4974ff297028519777ef8a34803e006ac7d99e23","sha256:24d3411434009ab7e455cf9c7f464c44aae0de966c71cea2ae5a3120be0df894"],"state_sha256":"058ff7f20b781d14016d23a16552e2638aeaf50e3ceabf634047bea38c64e78e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EzlYzVifd3ud1qNOZOCA11DB1GuPzymUJIFcDdRfg0/gVM01QGKZ0AdZ6RD3F/JBWCw5Q4YlQXwTh9Hvv5lYDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T06:54:06.103621Z","bundle_sha256":"a3fdc4f8ed8bea148d0cf58cf53b4f3b93cb70676e29c6914591921735a35a16"}}