{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:TEH6BTCB5FTL4MASMO4DXQMB5L","short_pith_number":"pith:TEH6BTCB","canonical_record":{"source":{"id":"2003.03109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-06T09:59:57Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d73fa99429568826ab300fa6da44aeddae318f6d173a329bb83beced67cfb093","abstract_canon_sha256":"c1835b946666fa94ca03125219768796a14f390d845377e5b611e940c0f06406"},"schema_version":"1.0"},"canonical_sha256":"990fe0cc41e966be301263b83bc181eac6ddf30b3092c977aac3392c96500267","source":{"kind":"arxiv","id":"2003.03109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.03109","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"arxiv_version","alias_value":"2003.03109v1","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.03109","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_12","alias_value":"TEH6BTCB5FTL","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_16","alias_value":"TEH6BTCB5FTL4MAS","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_8","alias_value":"TEH6BTCB","created_at":"2026-07-05T00:46:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:TEH6BTCB5FTL4MASMO4DXQMB5L","target":"record","payload":{"canonical_record":{"source":{"id":"2003.03109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-06T09:59:57Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d73fa99429568826ab300fa6da44aeddae318f6d173a329bb83beced67cfb093","abstract_canon_sha256":"c1835b946666fa94ca03125219768796a14f390d845377e5b611e940c0f06406"},"schema_version":"1.0"},"canonical_sha256":"990fe0cc41e966be301263b83bc181eac6ddf30b3092c977aac3392c96500267","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:46:10.031954Z","signature_b64":"BkXWlnnfV0C9LTIvKjyYLN5GmoK+2TxA+I2cGTXvoFJe47CR5N79ZYnLAkrwDmDYRcq/ootw14W/Sr/m2RIeAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"990fe0cc41e966be301263b83bc181eac6ddf30b3092c977aac3392c96500267","last_reissued_at":"2026-07-05T00:46:10.031567Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:46:10.031567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.03109","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":"2026-07-05T00:46:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hwIM2lSUDPfruzVpIW4XDGKXGsSTz+1SHTap1WmPFmYOAcMtINipz263gcSAOdo1bg24izvmt5AV6HZV+aCYDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:59:46.396544Z"},"content_sha256":"cf22f74a9088c090b5ab271c5fcab27452af05b3abc1932445423f4c38f7fce7","schema_version":"1.0","event_id":"sha256:cf22f74a9088c090b5ab271c5fcab27452af05b3abc1932445423f4c38f7fce7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:TEH6BTCB5FTL4MASMO4DXQMB5L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Brandon Chan, David Snead, Jevgenij Gamper, Nasir Rajpoot, Yee Wah Tsang","submitted_at":"2020-03-06T09:59:57Z","abstract_excerpt":"To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an abundance of healthy patients, versus a small variety of positive, abnormal cases. Moreover, the annotation of a positive sample requires time consuming input from medical domain experts. This scenario would suggest a promise for one-class classification type approaches. In this work we propose a general one-class classification model for histology, that is meta-trained on multiple histology datasets simultaneously, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.03109","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/2003.03109/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":"2026-07-05T00:46:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E59ky0DyQCAleKmJohUA7DNAyq1G+GEtjWtOEnuFH+fYv5EaaSh0xyXtm0SxmuF/gl96aFlRSNFOTYiRHOrCDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:59:46.396921Z"},"content_sha256":"6677365ef113321ad2fd5f479e370e7cb6fcf454ec1384dcf61b6eefc4b0c466","schema_version":"1.0","event_id":"sha256:6677365ef113321ad2fd5f479e370e7cb6fcf454ec1384dcf61b6eefc4b0c466"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/bundle.json","state_url":"https://pith.science/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/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":"2026-07-09T05:59:46Z","links":{"resolver":"https://pith.science/pith/TEH6BTCB5FTL4MASMO4DXQMB5L","bundle":"https://pith.science/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/bundle.json","state":"https://pith.science/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TEH6BTCB5FTL4MASMO4DXQMB5L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:TEH6BTCB5FTL4MASMO4DXQMB5L","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":"c1835b946666fa94ca03125219768796a14f390d845377e5b611e940c0f06406","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-06T09:59:57Z","title_canon_sha256":"d73fa99429568826ab300fa6da44aeddae318f6d173a329bb83beced67cfb093"},"schema_version":"1.0","source":{"id":"2003.03109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.03109","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"arxiv_version","alias_value":"2003.03109v1","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.03109","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_12","alias_value":"TEH6BTCB5FTL","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_16","alias_value":"TEH6BTCB5FTL4MAS","created_at":"2026-07-05T00:46:10Z"},{"alias_kind":"pith_short_8","alias_value":"TEH6BTCB","created_at":"2026-07-05T00:46:10Z"}],"graph_snapshots":[{"event_id":"sha256:6677365ef113321ad2fd5f479e370e7cb6fcf454ec1384dcf61b6eefc4b0c466","target":"graph","created_at":"2026-07-05T00:46:10Z","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/2003.03109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an abundance of healthy patients, versus a small variety of positive, abnormal cases. Moreover, the annotation of a positive sample requires time consuming input from medical domain experts. This scenario would suggest a promise for one-class classification type approaches. In this work we propose a general one-class classification model for histology, that is meta-trained on multiple histology datasets simultaneously, a","authors_text":"Brandon Chan, David Snead, Jevgenij Gamper, Nasir Rajpoot, Yee Wah Tsang","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-06T09:59:57Z","title":"Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.03109","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:cf22f74a9088c090b5ab271c5fcab27452af05b3abc1932445423f4c38f7fce7","target":"record","created_at":"2026-07-05T00:46:10Z","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":"c1835b946666fa94ca03125219768796a14f390d845377e5b611e940c0f06406","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-06T09:59:57Z","title_canon_sha256":"d73fa99429568826ab300fa6da44aeddae318f6d173a329bb83beced67cfb093"},"schema_version":"1.0","source":{"id":"2003.03109","kind":"arxiv","version":1}},"canonical_sha256":"990fe0cc41e966be301263b83bc181eac6ddf30b3092c977aac3392c96500267","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"990fe0cc41e966be301263b83bc181eac6ddf30b3092c977aac3392c96500267","first_computed_at":"2026-07-05T00:46:10.031567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:46:10.031567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BkXWlnnfV0C9LTIvKjyYLN5GmoK+2TxA+I2cGTXvoFJe47CR5N79ZYnLAkrwDmDYRcq/ootw14W/Sr/m2RIeAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:46:10.031954Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.03109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf22f74a9088c090b5ab271c5fcab27452af05b3abc1932445423f4c38f7fce7","sha256:6677365ef113321ad2fd5f479e370e7cb6fcf454ec1384dcf61b6eefc4b0c466"],"state_sha256":"7a25f1eabb569a6b20101e1996a676986ca1aab1b2325b92ddf14261e8ee0a98"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qBZcBJizFOcN8GVhXB4L6Ad7lHwf4RWMiTuNOou7C+AuqMjQBCMd935bFv6SAUBUx9V2XkHC06gcaH5zfErTCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:59:46.399246Z","bundle_sha256":"d8f392477a28988a191b679abc953bafd9ab8aee007da93c245219e7aac8c04b"}}