{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HYS6XEBQEBUSFFCKKIDC2FKGOD","short_pith_number":"pith:HYS6XEBQ","canonical_record":{"source":{"id":"1807.03095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-30T01:26:51Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"cdcd4a4e04a55658df02f09b16416bc0eb436df280c0cede01cbd8cb58ff0303","abstract_canon_sha256":"16b7140a64a771601643f6d79e346c305dc33cab0099f89a9f7c7cb1a6339b0a"},"schema_version":"1.0"},"canonical_sha256":"3e25eb9030206922944a52062d154670c3298b69e163bf8085dd8a3703d41d12","source":{"kind":"arxiv","id":"1807.03095","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03095","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03095v1","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03095","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"pith_short_12","alias_value":"HYS6XEBQEBUS","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HYS6XEBQEBUSFFCK","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HYS6XEBQ","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HYS6XEBQEBUSFFCKKIDC2FKGOD","target":"record","payload":{"canonical_record":{"source":{"id":"1807.03095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-30T01:26:51Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"cdcd4a4e04a55658df02f09b16416bc0eb436df280c0cede01cbd8cb58ff0303","abstract_canon_sha256":"16b7140a64a771601643f6d79e346c305dc33cab0099f89a9f7c7cb1a6339b0a"},"schema_version":"1.0"},"canonical_sha256":"3e25eb9030206922944a52062d154670c3298b69e163bf8085dd8a3703d41d12","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:12.918380Z","signature_b64":"2xi268sUWekgb9lbs3GebE2zPnvfOFoNjib+j1aaTxChQB+VABkWEB44FQAOJ90bWNbKjNmMJ5OeRD6vFS7VBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e25eb9030206922944a52062d154670c3298b69e163bf8085dd8a3703d41d12","last_reissued_at":"2026-05-18T00:11:12.917713Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:12.917713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.03095","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":"2026-05-18T00:11:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aJnQY8imUDXo45/isvb2S40mlgTdXmSmc33AHTWGtFxYtl161Z7W/FouWe1YX3NxOJ/sCXBqSPeEVGkxbAaFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T23:56:06.322721Z"},"content_sha256":"7be0252f8c87f676955ab1554499c3b6ac40720cf721440795a95c9ce93fd31d","schema_version":"1.0","event_id":"sha256:7be0252f8c87f676955ab1554499c3b6ac40720cf721440795a95c9ce93fd31d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HYS6XEBQEBUSFFCKKIDC2FKGOD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mammography Assessment using Multi-Scale Deep Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Khader Shameer, Lakshmi Subramanian, Ulzee An","submitted_at":"2018-06-30T01:26:51Z","abstract_excerpt":"Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in pushing beyond approximately bounding regions. We propose a classification approach grounded in high performance tissue assessment as an alternative to all-in-one localization and assessment models that is also capable of pinpointing the causal pixels. First, the objective of the mam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03095","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":""},"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":"2026-05-18T00:11:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j0Vs2sKmn5QR2CPJri5rv2FpFau6aDbKEmCfwjsgWN/OpCi/TD+muhltciAIIvY2XDle62zAxiW1J2ZVnRzjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T23:56:06.323056Z"},"content_sha256":"259cd92153314cdee2992695572fb5803f9fa9569c998122661b38b36ba1d338","schema_version":"1.0","event_id":"sha256:259cd92153314cdee2992695572fb5803f9fa9569c998122661b38b36ba1d338"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/bundle.json","state_url":"https://pith.science/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/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":"2026-07-08T23:56:06Z","links":{"resolver":"https://pith.science/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD","bundle":"https://pith.science/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/bundle.json","state":"https://pith.science/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HYS6XEBQEBUSFFCKKIDC2FKGOD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HYS6XEBQEBUSFFCKKIDC2FKGOD","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":"16b7140a64a771601643f6d79e346c305dc33cab0099f89a9f7c7cb1a6339b0a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-30T01:26:51Z","title_canon_sha256":"cdcd4a4e04a55658df02f09b16416bc0eb436df280c0cede01cbd8cb58ff0303"},"schema_version":"1.0","source":{"id":"1807.03095","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03095","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03095v1","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03095","created_at":"2026-05-18T00:11:12Z"},{"alias_kind":"pith_short_12","alias_value":"HYS6XEBQEBUS","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HYS6XEBQEBUSFFCK","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HYS6XEBQ","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:259cd92153314cdee2992695572fb5803f9fa9569c998122661b38b36ba1d338","target":"graph","created_at":"2026-05-18T00:11:12Z","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"},"paper":{"abstract_excerpt":"Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in pushing beyond approximately bounding regions. We propose a classification approach grounded in high performance tissue assessment as an alternative to all-in-one localization and assessment models that is also capable of pinpointing the causal pixels. First, the objective of the mam","authors_text":"Khader Shameer, Lakshmi Subramanian, Ulzee An","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-30T01:26:51Z","title":"Mammography Assessment using Multi-Scale Deep Classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03095","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":"sha256:7be0252f8c87f676955ab1554499c3b6ac40720cf721440795a95c9ce93fd31d","target":"record","created_at":"2026-05-18T00:11:12Z","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":"16b7140a64a771601643f6d79e346c305dc33cab0099f89a9f7c7cb1a6339b0a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-30T01:26:51Z","title_canon_sha256":"cdcd4a4e04a55658df02f09b16416bc0eb436df280c0cede01cbd8cb58ff0303"},"schema_version":"1.0","source":{"id":"1807.03095","kind":"arxiv","version":1}},"canonical_sha256":"3e25eb9030206922944a52062d154670c3298b69e163bf8085dd8a3703d41d12","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e25eb9030206922944a52062d154670c3298b69e163bf8085dd8a3703d41d12","first_computed_at":"2026-05-18T00:11:12.917713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:12.917713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2xi268sUWekgb9lbs3GebE2zPnvfOFoNjib+j1aaTxChQB+VABkWEB44FQAOJ90bWNbKjNmMJ5OeRD6vFS7VBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:12.918380Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.03095","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7be0252f8c87f676955ab1554499c3b6ac40720cf721440795a95c9ce93fd31d","sha256:259cd92153314cdee2992695572fb5803f9fa9569c998122661b38b36ba1d338"],"state_sha256":"1bf0e3780dbaeb677a148bdca8991eba2df1a7520365b21efd2170025fe67b7a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"27jtN0mT8mOUjITqzOGVLayqB6iSEDBEmDE6fwGHwBi+S1k1aqom/leFYU05qnJEh0+y+uLodY9ChiXYBWCvDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T23:56:06.325089Z","bundle_sha256":"7d050c0451da654136f7134f088ada548f16823b9861834ac31a969e94925161"}}