{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HEUPJTJHCQFHUIU526TTBR3IMU","short_pith_number":"pith:HEUPJTJH","schema_version":"1.0","canonical_sha256":"3928f4cd27140a7a229dd7a730c768653d5b430846e6788165f0708ec406702c","source":{"kind":"arxiv","id":"2308.06432","version":1},"attestation_state":"computed","paper":{"title":"Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Kun Huang, Mingchao Li, Qiang Chen, Songtao Yuan, Yuhan Zhang","submitted_at":"2023-08-12T01:40:23Z","abstract_excerpt":"Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions. Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular a"},"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":"2308.06432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-12T01:40:23Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"4e20164c9f64b8fe2de1099ae8ef2fd8e963a68462e083d214761a4300c48532","abstract_canon_sha256":"9d40f75e1bd357dfef4a718018fff34253d588fb05d0a67e5d6da0459a2c2f7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:39.991117Z","signature_b64":"oJ7L/ryk46CB+iW/KhA22ZJEfPs5td+a9Ljb/mrj6ZLgbnrAM5lSCATrp/yteq6XJfWkoP+eWxl92IkRq2v0AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3928f4cd27140a7a229dd7a730c768653d5b430846e6788165f0708ec406702c","last_reissued_at":"2026-07-05T06:40:39.990681Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:39.990681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Kun Huang, Mingchao Li, Qiang Chen, Songtao Yuan, Yuhan Zhang","submitted_at":"2023-08-12T01:40:23Z","abstract_excerpt":"Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions. Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06432","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/2308.06432/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":"2308.06432","created_at":"2026-07-05T06:40:39.990740+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06432v1","created_at":"2026-07-05T06:40:39.990740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06432","created_at":"2026-07-05T06:40:39.990740+00:00"},{"alias_kind":"pith_short_12","alias_value":"HEUPJTJHCQFH","created_at":"2026-07-05T06:40:39.990740+00:00"},{"alias_kind":"pith_short_16","alias_value":"HEUPJTJHCQFHUIU5","created_at":"2026-07-05T06:40:39.990740+00:00"},{"alias_kind":"pith_short_8","alias_value":"HEUPJTJH","created_at":"2026-07-05T06:40:39.990740+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/HEUPJTJHCQFHUIU526TTBR3IMU","json":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU.json","graph_json":"https://pith.science/api/pith-number/HEUPJTJHCQFHUIU526TTBR3IMU/graph.json","events_json":"https://pith.science/api/pith-number/HEUPJTJHCQFHUIU526TTBR3IMU/events.json","paper":"https://pith.science/paper/HEUPJTJH"},"agent_actions":{"view_html":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU","download_json":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU.json","view_paper":"https://pith.science/paper/HEUPJTJH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06432&json=true","fetch_graph":"https://pith.science/api/pith-number/HEUPJTJHCQFHUIU526TTBR3IMU/graph.json","fetch_events":"https://pith.science/api/pith-number/HEUPJTJHCQFHUIU526TTBR3IMU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU/action/storage_attestation","attest_author":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU/action/author_attestation","sign_citation":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU/action/citation_signature","submit_replication":"https://pith.science/pith/HEUPJTJHCQFHUIU526TTBR3IMU/action/replication_record"}},"created_at":"2026-07-05T06:40:39.990740+00:00","updated_at":"2026-07-05T06:40:39.990740+00:00"}