{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:XQFR4LYSXYEYG5LPJMMIDACOMI","short_pith_number":"pith:XQFR4LYS","canonical_record":{"source":{"id":"1807.04893","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-13T02:34:24Z","cross_cats_sorted":[],"title_canon_sha256":"ad0b8520a15cd6de98c546c64f0342a0e63c898690539c0a1e5c12f7f514e4f6","abstract_canon_sha256":"ca9c6196998a58ab731d2dccbe76203da4241026b1d09f7554408e961677763a"},"schema_version":"1.0"},"canonical_sha256":"bc0b1e2f12be0983756f4b1881804e6216464880813ae027c20f23a6fda3815e","source":{"kind":"arxiv","id":"1807.04893","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04893","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04893v1","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04893","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"XQFR4LYSXYEY","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XQFR4LYSXYEYG5LP","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XQFR4LYS","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:XQFR4LYSXYEYG5LPJMMIDACOMI","target":"record","payload":{"canonical_record":{"source":{"id":"1807.04893","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-13T02:34:24Z","cross_cats_sorted":[],"title_canon_sha256":"ad0b8520a15cd6de98c546c64f0342a0e63c898690539c0a1e5c12f7f514e4f6","abstract_canon_sha256":"ca9c6196998a58ab731d2dccbe76203da4241026b1d09f7554408e961677763a"},"schema_version":"1.0"},"canonical_sha256":"bc0b1e2f12be0983756f4b1881804e6216464880813ae027c20f23a6fda3815e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:49.491952Z","signature_b64":"hOoRnVsNY9jW3AZZpQ5hG1NGrGNxp/kQFFMoFQaOhzrXACF0IPZm/kmdw6tygD2/5JF7XdUangFjXESp3L55Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc0b1e2f12be0983756f4b1881804e6216464880813ae027c20f23a6fda3815e","last_reissued_at":"2026-05-18T00:10:49.491458Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:49.491458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.04893","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":"2026-05-18T00:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C/ltmG338npM45urtTqup57yZ5DIUxFHrm9X5wxI8fPuBd1R6w4/HHCg0VWTfpbA7VzF1YAUiW3szyP3Nw6sAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:44:14.190117Z"},"content_sha256":"419084df3eea8855a9703fff021c0b1662823f2b03d4fcf5b05ceee5bb98fccd","schema_version":"1.0","event_id":"sha256:419084df3eea8855a9703fff021c0b1662823f2b03d4fcf5b05ceee5bb98fccd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:XQFR4LYSXYEYG5LPJMMIDACOMI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic segmentation of skin lesions using deep learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jagath C. Rajapakse, Joshua Peter Ebenezer","submitted_at":"2018-07-13T02:34:24Z","abstract_excerpt":"This paper summarizes the method used in our submission to Task 1 of the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We used a fully automated method to accurately segment lesion boundaries from dermoscopic images. A U-net deep learning network is trained on publicly available data from ISIC. We introduce the use of intensity, color, and texture enhancement operations as pre-processing steps and morphological operations and contour identification as post-processing steps."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04893","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":"2026-05-18T00:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xItrZNHI5jTVwCbtPrvQfYJHb07+lLiOoKJ/unqIU6EvhXDzR96ymlfbjEpNJsNeYxPVstqEovGkbUg2hhaEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:44:14.190832Z"},"content_sha256":"977747f3029ec6aff87791ba49d0c4dd6552c8d5d51c3183a313a603aec1931d","schema_version":"1.0","event_id":"sha256:977747f3029ec6aff87791ba49d0c4dd6552c8d5d51c3183a313a603aec1931d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/bundle.json","state_url":"https://pith.science/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/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":"2026-05-25T20:44:14Z","links":{"resolver":"https://pith.science/pith/XQFR4LYSXYEYG5LPJMMIDACOMI","bundle":"https://pith.science/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/bundle.json","state":"https://pith.science/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XQFR4LYSXYEYG5LPJMMIDACOMI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:XQFR4LYSXYEYG5LPJMMIDACOMI","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":"ca9c6196998a58ab731d2dccbe76203da4241026b1d09f7554408e961677763a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-13T02:34:24Z","title_canon_sha256":"ad0b8520a15cd6de98c546c64f0342a0e63c898690539c0a1e5c12f7f514e4f6"},"schema_version":"1.0","source":{"id":"1807.04893","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04893","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04893v1","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04893","created_at":"2026-05-18T00:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"XQFR4LYSXYEY","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XQFR4LYSXYEYG5LP","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XQFR4LYS","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:977747f3029ec6aff87791ba49d0c4dd6552c8d5d51c3183a313a603aec1931d","target":"graph","created_at":"2026-05-18T00:10:49Z","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":"This paper summarizes the method used in our submission to Task 1 of the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We used a fully automated method to accurately segment lesion boundaries from dermoscopic images. A U-net deep learning network is trained on publicly available data from ISIC. We introduce the use of intensity, color, and texture enhancement operations as pre-processing steps and morphological operations and contour identification as post-processing steps.","authors_text":"Jagath C. Rajapakse, Joshua Peter Ebenezer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-13T02:34:24Z","title":"Automatic segmentation of skin lesions using deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04893","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":"sha256:419084df3eea8855a9703fff021c0b1662823f2b03d4fcf5b05ceee5bb98fccd","target":"record","created_at":"2026-05-18T00:10:49Z","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":"ca9c6196998a58ab731d2dccbe76203da4241026b1d09f7554408e961677763a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-13T02:34:24Z","title_canon_sha256":"ad0b8520a15cd6de98c546c64f0342a0e63c898690539c0a1e5c12f7f514e4f6"},"schema_version":"1.0","source":{"id":"1807.04893","kind":"arxiv","version":1}},"canonical_sha256":"bc0b1e2f12be0983756f4b1881804e6216464880813ae027c20f23a6fda3815e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc0b1e2f12be0983756f4b1881804e6216464880813ae027c20f23a6fda3815e","first_computed_at":"2026-05-18T00:10:49.491458Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:49.491458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hOoRnVsNY9jW3AZZpQ5hG1NGrGNxp/kQFFMoFQaOhzrXACF0IPZm/kmdw6tygD2/5JF7XdUangFjXESp3L55Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:49.491952Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.04893","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:419084df3eea8855a9703fff021c0b1662823f2b03d4fcf5b05ceee5bb98fccd","sha256:977747f3029ec6aff87791ba49d0c4dd6552c8d5d51c3183a313a603aec1931d"],"state_sha256":"2be19e2fcf79796a85208532d1bb0366a106e3365428f0630b83bf1b158c43ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YzMhChoNYss0CzVuMm7//kOmFOMXM6L+Ll0kpy5Qi0NQH74gp6V+8GYsrPnVEpR8k0ym3NJ0QwFlkkqVnBzEAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:44:14.194676Z","bundle_sha256":"8ecf1e3ec58aab65295d7c475a79ef9679b41bc27379037f2f7a7588a6002916"}}