{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:JZWNLPQZF6F2YFVUWJCAYJQAWW","short_pith_number":"pith:JZWNLPQZ","schema_version":"1.0","canonical_sha256":"4e6cd5be192f8bac16b4b2440c2600b58d9be6bb661ab8ebc837e62db59a13aa","source":{"kind":"arxiv","id":"2103.04207","version":1},"attestation_state":"computed","paper":{"title":"Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Nasser Kehtarnavaz, Sharmin Majumder","submitted_at":"2021-03-06T23:06:46Z","abstract_excerpt":"This paper presents a multitask deep learning model to detect all the five stages of diabetic retinopathy (DR) consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This multitask model consists of one classification model and one regression model, each with its own loss function. Noting that a higher severity level normally occurs after a lower severity level, this dependency is taken into consideration by concatenating the classification and regression models. The regression model learns the inter-dependency between the stages and outputs a score corresponding to the seve"},"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":"2103.04207","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-06T23:06:46Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"c6f499ce3ac9be3ad9ae3c941041a6a628d84c333fe5978cd43565cf74106531","abstract_canon_sha256":"c4882c6b55f046da491d871a601b0de80413e5841fb94e735f0aa355af68ae0f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:39:57.518329Z","signature_b64":"B6GFReMegfaUkUbwG2/TEQlbTc+LQKk0KAD3POEXromGux0KCHKttmxYto8V3Z78qtBGKs5Nu/vAriHgsDOhBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e6cd5be192f8bac16b4b2440c2600b58d9be6bb661ab8ebc837e62db59a13aa","last_reissued_at":"2026-07-05T03:39:57.517849Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:39:57.517849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Nasser Kehtarnavaz, Sharmin Majumder","submitted_at":"2021-03-06T23:06:46Z","abstract_excerpt":"This paper presents a multitask deep learning model to detect all the five stages of diabetic retinopathy (DR) consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This multitask model consists of one classification model and one regression model, each with its own loss function. Noting that a higher severity level normally occurs after a lower severity level, this dependency is taken into consideration by concatenating the classification and regression models. The regression model learns the inter-dependency between the stages and outputs a score corresponding to the seve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.04207","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/2103.04207/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":"2103.04207","created_at":"2026-07-05T03:39:57.517901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.04207v1","created_at":"2026-07-05T03:39:57.517901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.04207","created_at":"2026-07-05T03:39:57.517901+00:00"},{"alias_kind":"pith_short_12","alias_value":"JZWNLPQZF6F2","created_at":"2026-07-05T03:39:57.517901+00:00"},{"alias_kind":"pith_short_16","alias_value":"JZWNLPQZF6F2YFVU","created_at":"2026-07-05T03:39:57.517901+00:00"},{"alias_kind":"pith_short_8","alias_value":"JZWNLPQZ","created_at":"2026-07-05T03:39:57.517901+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/JZWNLPQZF6F2YFVUWJCAYJQAWW","json":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW.json","graph_json":"https://pith.science/api/pith-number/JZWNLPQZF6F2YFVUWJCAYJQAWW/graph.json","events_json":"https://pith.science/api/pith-number/JZWNLPQZF6F2YFVUWJCAYJQAWW/events.json","paper":"https://pith.science/paper/JZWNLPQZ"},"agent_actions":{"view_html":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW","download_json":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW.json","view_paper":"https://pith.science/paper/JZWNLPQZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.04207&json=true","fetch_graph":"https://pith.science/api/pith-number/JZWNLPQZF6F2YFVUWJCAYJQAWW/graph.json","fetch_events":"https://pith.science/api/pith-number/JZWNLPQZF6F2YFVUWJCAYJQAWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW/action/storage_attestation","attest_author":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW/action/author_attestation","sign_citation":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW/action/citation_signature","submit_replication":"https://pith.science/pith/JZWNLPQZF6F2YFVUWJCAYJQAWW/action/replication_record"}},"created_at":"2026-07-05T03:39:57.517901+00:00","updated_at":"2026-07-05T03:39:57.517901+00:00"}