{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EDYFDYJ4GE44D64LNWM2BING4C","short_pith_number":"pith:EDYFDYJ4","canonical_record":{"source":{"id":"1804.00177","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T14:13:43Z","cross_cats_sorted":[],"title_canon_sha256":"c7a4c82db1fc1ddc93e76edca698c1c407a85ad5e7e2f6ba7f2a966a00403f89","abstract_canon_sha256":"b8804f6fbbd4a1b8ccd01757ded0652d7def6e0fa7c8542a176fde756941297b"},"schema_version":"1.0"},"canonical_sha256":"20f051e13c3139c1fb8b6d99a0a1a6e0bac72fee094bea69f1649455ac3370ce","source":{"kind":"arxiv","id":"1804.00177","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00177","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00177v2","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00177","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"pith_short_12","alias_value":"EDYFDYJ4GE44","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EDYFDYJ4GE44D64L","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EDYFDYJ4","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EDYFDYJ4GE44D64LNWM2BING4C","target":"record","payload":{"canonical_record":{"source":{"id":"1804.00177","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T14:13:43Z","cross_cats_sorted":[],"title_canon_sha256":"c7a4c82db1fc1ddc93e76edca698c1c407a85ad5e7e2f6ba7f2a966a00403f89","abstract_canon_sha256":"b8804f6fbbd4a1b8ccd01757ded0652d7def6e0fa7c8542a176fde756941297b"},"schema_version":"1.0"},"canonical_sha256":"20f051e13c3139c1fb8b6d99a0a1a6e0bac72fee094bea69f1649455ac3370ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:38.273956Z","signature_b64":"EljRZHHewGam0O/zE4Rvn0EXtNSK0MtlUmZLb8SKmpSMy3VhGGJpGJn31bG/X3Ar6ZWBPf/kjUKn/HaPTBqtDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20f051e13c3139c1fb8b6d99a0a1a6e0bac72fee094bea69f1649455ac3370ce","last_reissued_at":"2026-05-17T23:44:38.273498Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:38.273498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.00177","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":"2026-05-17T23:44:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0o9sh5iuQ2btBVWOUF1gVb8nfcWEoK/gHOxvhiFE3XLMxGevRjBCZKyVtsiuTfvkcM97K5QAuuRyPsmksu6TAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:21:16.047532Z"},"content_sha256":"21566fa532f1fd4826cd0561963aa16a2e73e804ecc848c27ef0fe0574bda147","schema_version":"1.0","event_id":"sha256:21566fa532f1fd4826cd0561963aa16a2e73e804ecc848c27ef0fe0574bda147"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EDYFDYJ4GE44D64LNWM2BING4C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Webly Supervised Learning for Skin Lesion Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Federico Tombari, Fernando Navarro, Nassir Navab, Sailesh Conjeti","submitted_at":"2018-03-31T14:13:43Z","abstract_excerpt":"Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training process with a robust loss function, termed as Webly Supervised Learning (WSL) to train deep models for the task. We also leverage search by image to improve the search specificity of our web-crawling an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00177","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":""},"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":"2026-05-17T23:44:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"81z+pMTI98ENCt9Tg0mXHIefcYLuOAeaesAqcglMksYWoP0mgbrIxeZO2MU9FXNbDZyPfRz4Z4Y6xokjAdCkAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:21:16.048087Z"},"content_sha256":"3a08a6c71aaf6feb9b9cd99a89d1f691fecac5534df6930270a7d621d925d49a","schema_version":"1.0","event_id":"sha256:3a08a6c71aaf6feb9b9cd99a89d1f691fecac5534df6930270a7d621d925d49a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EDYFDYJ4GE44D64LNWM2BING4C/bundle.json","state_url":"https://pith.science/pith/EDYFDYJ4GE44D64LNWM2BING4C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EDYFDYJ4GE44D64LNWM2BING4C/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":"2026-05-28T05:21:16Z","links":{"resolver":"https://pith.science/pith/EDYFDYJ4GE44D64LNWM2BING4C","bundle":"https://pith.science/pith/EDYFDYJ4GE44D64LNWM2BING4C/bundle.json","state":"https://pith.science/pith/EDYFDYJ4GE44D64LNWM2BING4C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EDYFDYJ4GE44D64LNWM2BING4C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EDYFDYJ4GE44D64LNWM2BING4C","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":"b8804f6fbbd4a1b8ccd01757ded0652d7def6e0fa7c8542a176fde756941297b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T14:13:43Z","title_canon_sha256":"c7a4c82db1fc1ddc93e76edca698c1c407a85ad5e7e2f6ba7f2a966a00403f89"},"schema_version":"1.0","source":{"id":"1804.00177","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00177","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00177v2","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00177","created_at":"2026-05-17T23:44:38Z"},{"alias_kind":"pith_short_12","alias_value":"EDYFDYJ4GE44","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EDYFDYJ4GE44D64L","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EDYFDYJ4","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:3a08a6c71aaf6feb9b9cd99a89d1f691fecac5534df6930270a7d621d925d49a","target":"graph","created_at":"2026-05-17T23:44:38Z","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":"Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training process with a robust loss function, termed as Webly Supervised Learning (WSL) to train deep models for the task. We also leverage search by image to improve the search specificity of our web-crawling an","authors_text":"Federico Tombari, Fernando Navarro, Nassir Navab, Sailesh Conjeti","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T14:13:43Z","title":"Webly Supervised Learning for Skin Lesion Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00177","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":"sha256:21566fa532f1fd4826cd0561963aa16a2e73e804ecc848c27ef0fe0574bda147","target":"record","created_at":"2026-05-17T23:44:38Z","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":"b8804f6fbbd4a1b8ccd01757ded0652d7def6e0fa7c8542a176fde756941297b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T14:13:43Z","title_canon_sha256":"c7a4c82db1fc1ddc93e76edca698c1c407a85ad5e7e2f6ba7f2a966a00403f89"},"schema_version":"1.0","source":{"id":"1804.00177","kind":"arxiv","version":2}},"canonical_sha256":"20f051e13c3139c1fb8b6d99a0a1a6e0bac72fee094bea69f1649455ac3370ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"20f051e13c3139c1fb8b6d99a0a1a6e0bac72fee094bea69f1649455ac3370ce","first_computed_at":"2026-05-17T23:44:38.273498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:38.273498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EljRZHHewGam0O/zE4Rvn0EXtNSK0MtlUmZLb8SKmpSMy3VhGGJpGJn31bG/X3Ar6ZWBPf/kjUKn/HaPTBqtDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:38.273956Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.00177","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21566fa532f1fd4826cd0561963aa16a2e73e804ecc848c27ef0fe0574bda147","sha256:3a08a6c71aaf6feb9b9cd99a89d1f691fecac5534df6930270a7d621d925d49a"],"state_sha256":"c07f81d0cc25f0ad64d0bfcfed4f9a4a79c5fc88e97f928104daf7b97a7648bc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"InvdkNlsPEaDtdixEiBDy1estdxxt3pOLm8Al+xaegjicM26cZNMyhO84uYrQPu91Roi6WkjvxhbAdgS6PBmCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T05:21:16.050888Z","bundle_sha256":"3888948503575c53d6b722b2f52995a66c6af76a125ce1b476d170ac1db8afde"}}