{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KUAK27E4DSGPDVC47PVAAKKJ4S","short_pith_number":"pith:KUAK27E4","schema_version":"1.0","canonical_sha256":"5500ad7c9c1c8cf1d45cfbea002949e4a86afe52c2a4ac1936d5891551683695","source":{"kind":"arxiv","id":"1907.09180","version":1},"attestation_state":"computed","paper":{"title":"Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hemin Ali Qadir, Ilangko Balasingham, Jacob Bergsland, Johannes Solhusvik, Lars Aabakken, Younghak Shin","submitted_at":"2019-07-22T08:34:47Z","abstract_excerpt":"Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extr"},"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":"1907.09180","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-22T08:34:47Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"da8352eabb7b8af3f6b5c603524b94529633308cb285ab2e3f3cae45b6581d91","abstract_canon_sha256":"73354f1be22ed2ce967159f5d93d6cebfb241bd5cda3c702f8771660d0b93051"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:59.932559Z","signature_b64":"hmJZfAzY/8CSc0B5kroDNtabQTbq/5tvj1mZR81r/ZgqaPeBzLvfB4SUEk0WU/FAi7AOVrE3D04k/gweGQrEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5500ad7c9c1c8cf1d45cfbea002949e4a86afe52c2a4ac1936d5891551683695","last_reissued_at":"2026-05-17T23:39:59.932021Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:59.932021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hemin Ali Qadir, Ilangko Balasingham, Jacob Bergsland, Johannes Solhusvik, Lars Aabakken, Younghak Shin","submitted_at":"2019-07-22T08:34:47Z","abstract_excerpt":"Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.09180","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":""},"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":"1907.09180","created_at":"2026-05-17T23:39:59.932098+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.09180v1","created_at":"2026-05-17T23:39:59.932098+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.09180","created_at":"2026-05-17T23:39:59.932098+00:00"},{"alias_kind":"pith_short_12","alias_value":"KUAK27E4DSGP","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KUAK27E4DSGPDVC4","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KUAK27E4","created_at":"2026-05-18T12:33:21.387695+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/KUAK27E4DSGPDVC47PVAAKKJ4S","json":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S.json","graph_json":"https://pith.science/api/pith-number/KUAK27E4DSGPDVC47PVAAKKJ4S/graph.json","events_json":"https://pith.science/api/pith-number/KUAK27E4DSGPDVC47PVAAKKJ4S/events.json","paper":"https://pith.science/paper/KUAK27E4"},"agent_actions":{"view_html":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S","download_json":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S.json","view_paper":"https://pith.science/paper/KUAK27E4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.09180&json=true","fetch_graph":"https://pith.science/api/pith-number/KUAK27E4DSGPDVC47PVAAKKJ4S/graph.json","fetch_events":"https://pith.science/api/pith-number/KUAK27E4DSGPDVC47PVAAKKJ4S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S/action/storage_attestation","attest_author":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S/action/author_attestation","sign_citation":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S/action/citation_signature","submit_replication":"https://pith.science/pith/KUAK27E4DSGPDVC47PVAAKKJ4S/action/replication_record"}},"created_at":"2026-05-17T23:39:59.932098+00:00","updated_at":"2026-05-17T23:39:59.932098+00:00"}