{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7VDYRHYSCPNNBM6EKXQD7MUS2K","short_pith_number":"pith:7VDYRHYS","schema_version":"1.0","canonical_sha256":"fd47889f1213dad0b3c455e03fb292d291a330655bcc7e65c8ef48a54deb1b8b","source":{"kind":"arxiv","id":"1911.02475","version":1},"attestation_state":"computed","paper":{"title":"Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Lu Jun, Xiaofeng Liu, Xu Han, Yi Ge, Yukai Qiao","submitted_at":"2019-11-03T20:41:14Z","abstract_excerpt":"The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR (0), mild DR (1), moderate DR (2), severe DR (3) and proliferative DR (4). This labeling system is common for medical disease. Previous methods usually construct a multi-binary-classification task or propose some re-parameter schemes in the output unit. In this paper, we target on this task from the perspective of loss function. More specifically, the Wasserstein distance is utilized as an alternative, explicitly in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1911.02475","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-11-03T20:41:14Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d699c7096dfdca5acf95f7f7910368233c9318b46b5e2bd74a1e0fbec1a34012","abstract_canon_sha256":"51bb4d7e1866ed928f86a0995184865aef821633704c27f2329a0a4aad21d8f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:17:34.630300Z","signature_b64":"qEc6fT2oXxVaUdD12xjz3xDLdPzY4ezb6X4hFhHnieK/KkmVzquV4XsM11nk1kNJ1tNmJX9pk4hXgYgFQBC7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd47889f1213dad0b3c455e03fb292d291a330655bcc7e65c8ef48a54deb1b8b","last_reissued_at":"2026-07-05T00:17:34.629901Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:17:34.629901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Lu Jun, Xiaofeng Liu, Xu Han, Yi Ge, Yukai Qiao","submitted_at":"2019-11-03T20:41:14Z","abstract_excerpt":"The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR (0), mild DR (1), moderate DR (2), severe DR (3) and proliferative DR (4). This labeling system is common for medical disease. Previous methods usually construct a multi-binary-classification task or propose some re-parameter schemes in the output unit. In this paper, we target on this task from the perspective of loss function. More specifically, the Wasserstein distance is utilized as an alternative, explicitly in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.02475","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/1911.02475/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1911.02475","created_at":"2026-07-05T00:17:34.629958+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.02475v1","created_at":"2026-07-05T00:17:34.629958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.02475","created_at":"2026-07-05T00:17:34.629958+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VDYRHYSCPNN","created_at":"2026-07-05T00:17:34.629958+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VDYRHYSCPNNBM6E","created_at":"2026-07-05T00:17:34.629958+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VDYRHYS","created_at":"2026-07-05T00:17:34.629958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K","json":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K.json","graph_json":"https://pith.science/api/pith-number/7VDYRHYSCPNNBM6EKXQD7MUS2K/graph.json","events_json":"https://pith.science/api/pith-number/7VDYRHYSCPNNBM6EKXQD7MUS2K/events.json","paper":"https://pith.science/paper/7VDYRHYS"},"agent_actions":{"view_html":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K","download_json":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K.json","view_paper":"https://pith.science/paper/7VDYRHYS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.02475&json=true","fetch_graph":"https://pith.science/api/pith-number/7VDYRHYSCPNNBM6EKXQD7MUS2K/graph.json","fetch_events":"https://pith.science/api/pith-number/7VDYRHYSCPNNBM6EKXQD7MUS2K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K/action/storage_attestation","attest_author":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K/action/author_attestation","sign_citation":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K/action/citation_signature","submit_replication":"https://pith.science/pith/7VDYRHYSCPNNBM6EKXQD7MUS2K/action/replication_record"}},"created_at":"2026-07-05T00:17:34.629958+00:00","updated_at":"2026-07-05T00:17:34.629958+00:00"}