{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:N3CFTQNVLE6FOULSSQAUJNXNED","short_pith_number":"pith:N3CFTQNV","schema_version":"1.0","canonical_sha256":"6ec459c1b5593c575172940144b6ed20c0ee42b551ad6a714742f84afa3a1b7f","source":{"kind":"arxiv","id":"2302.02289","version":1},"attestation_state":"computed","paper":{"title":"Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Aliasghar Mortazi, Elif Keles, Ulas Bagci, Vedat Cicek","submitted_at":"2023-02-05T03:49:27Z","abstract_excerpt":"The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in sho"},"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":"2302.02289","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-02-05T03:49:27Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"b7fa42a10504a244c3e187af8729a7d7d317ce4fd483a7c5d12961ae6848b2c7","abstract_canon_sha256":"d8415f5fd70a2e5cea63027fec3d43a45d28f746c00a918e503d52eea213c621"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:38:51.415687Z","signature_b64":"5DvpWM2384L5QWqC5a4IfetFPomnffRU9x7dz5+iiQGp8e7CUL+vfw0Wi3LZA8etRvyW8MLVPrTut5+1fz+nCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ec459c1b5593c575172940144b6ed20c0ee42b551ad6a714742f84afa3a1b7f","last_reissued_at":"2026-07-05T05:38:51.415260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:38:51.415260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Aliasghar Mortazi, Elif Keles, Ulas Bagci, Vedat Cicek","submitted_at":"2023-02-05T03:49:27Z","abstract_excerpt":"The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02289","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/2302.02289/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":"2302.02289","created_at":"2026-07-05T05:38:51.415321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.02289v1","created_at":"2026-07-05T05:38:51.415321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02289","created_at":"2026-07-05T05:38:51.415321+00:00"},{"alias_kind":"pith_short_12","alias_value":"N3CFTQNVLE6F","created_at":"2026-07-05T05:38:51.415321+00:00"},{"alias_kind":"pith_short_16","alias_value":"N3CFTQNVLE6FOULS","created_at":"2026-07-05T05:38:51.415321+00:00"},{"alias_kind":"pith_short_8","alias_value":"N3CFTQNV","created_at":"2026-07-05T05:38:51.415321+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/N3CFTQNVLE6FOULSSQAUJNXNED","json":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED.json","graph_json":"https://pith.science/api/pith-number/N3CFTQNVLE6FOULSSQAUJNXNED/graph.json","events_json":"https://pith.science/api/pith-number/N3CFTQNVLE6FOULSSQAUJNXNED/events.json","paper":"https://pith.science/paper/N3CFTQNV"},"agent_actions":{"view_html":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED","download_json":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED.json","view_paper":"https://pith.science/paper/N3CFTQNV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.02289&json=true","fetch_graph":"https://pith.science/api/pith-number/N3CFTQNVLE6FOULSSQAUJNXNED/graph.json","fetch_events":"https://pith.science/api/pith-number/N3CFTQNVLE6FOULSSQAUJNXNED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED/action/storage_attestation","attest_author":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED/action/author_attestation","sign_citation":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED/action/citation_signature","submit_replication":"https://pith.science/pith/N3CFTQNVLE6FOULSSQAUJNXNED/action/replication_record"}},"created_at":"2026-07-05T05:38:51.415321+00:00","updated_at":"2026-07-05T05:38:51.415321+00:00"}