{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FNIVO4A5V4UGMFXQN3BK7UFL7T","short_pith_number":"pith:FNIVO4A5","schema_version":"1.0","canonical_sha256":"2b5157701daf286616f06ec2afd0abfceb054f29223bbee69efc6a2ad8c14a59","source":{"kind":"arxiv","id":"2412.14100","version":1},"attestation_state":"computed","paper":{"title":"Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Bijay Adhikari, Bishesh Khanal, Confidence Raymond, Dong Zhang, Jakesh Bohaju, Laxmi Kanta Poudel, Mahesh Shakya, Pratibha Kulung, Udunna C Anazodo","submitted_at":"2024-12-18T17:48:32Z","abstract_excerpt":"Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications in low-resource settings which brings a unique set of challenges including scarcity of data. As a step towards solving these specific problems, we propose Convolutional adapter-inspired Parameter-efficient Fine-tuning (PEFT) of MedNeXt architecture. To validate our idea, we show our method performs comparable to full fine-tuning with the added benefit of reduced training compute using BraTS-2021 as pre-training datas"},"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":"2412.14100","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-12-18T17:48:32Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"5b882775bebfbc5d0e1b97d9ab82900f7a7d159ca91b9f76c3151e68ee7873e5","abstract_canon_sha256":"93e05e490f4de7fabf04a652ff8cb5156e599737b05565a15b84fd637b2507db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:13.709794Z","signature_b64":"OMAE64HeB6Z8LBjiUGwoWVsKiOj57Hi9PuSxZL9hKlEYGO4hrBzFEcRGfLAl92wqQnhSmeexKlTYNHsnSOZqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b5157701daf286616f06ec2afd0abfceb054f29223bbee69efc6a2ad8c14a59","last_reissued_at":"2026-07-05T09:51:13.709294Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:13.709294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Bijay Adhikari, Bishesh Khanal, Confidence Raymond, Dong Zhang, Jakesh Bohaju, Laxmi Kanta Poudel, Mahesh Shakya, Pratibha Kulung, Udunna C Anazodo","submitted_at":"2024-12-18T17:48:32Z","abstract_excerpt":"Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications in low-resource settings which brings a unique set of challenges including scarcity of data. As a step towards solving these specific problems, we propose Convolutional adapter-inspired Parameter-efficient Fine-tuning (PEFT) of MedNeXt architecture. To validate our idea, we show our method performs comparable to full fine-tuning with the added benefit of reduced training compute using BraTS-2021 as pre-training datas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14100","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/2412.14100/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":"2412.14100","created_at":"2026-07-05T09:51:13.709367+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.14100v1","created_at":"2026-07-05T09:51:13.709367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14100","created_at":"2026-07-05T09:51:13.709367+00:00"},{"alias_kind":"pith_short_12","alias_value":"FNIVO4A5V4UG","created_at":"2026-07-05T09:51:13.709367+00:00"},{"alias_kind":"pith_short_16","alias_value":"FNIVO4A5V4UGMFXQ","created_at":"2026-07-05T09:51:13.709367+00:00"},{"alias_kind":"pith_short_8","alias_value":"FNIVO4A5","created_at":"2026-07-05T09:51:13.709367+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/FNIVO4A5V4UGMFXQN3BK7UFL7T","json":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T.json","graph_json":"https://pith.science/api/pith-number/FNIVO4A5V4UGMFXQN3BK7UFL7T/graph.json","events_json":"https://pith.science/api/pith-number/FNIVO4A5V4UGMFXQN3BK7UFL7T/events.json","paper":"https://pith.science/paper/FNIVO4A5"},"agent_actions":{"view_html":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T","download_json":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T.json","view_paper":"https://pith.science/paper/FNIVO4A5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.14100&json=true","fetch_graph":"https://pith.science/api/pith-number/FNIVO4A5V4UGMFXQN3BK7UFL7T/graph.json","fetch_events":"https://pith.science/api/pith-number/FNIVO4A5V4UGMFXQN3BK7UFL7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T/action/storage_attestation","attest_author":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T/action/author_attestation","sign_citation":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T/action/citation_signature","submit_replication":"https://pith.science/pith/FNIVO4A5V4UGMFXQN3BK7UFL7T/action/replication_record"}},"created_at":"2026-07-05T09:51:13.709367+00:00","updated_at":"2026-07-05T09:51:13.709367+00:00"}