{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4FCZV5GEYMOD4HEFYXNYDDVG4O","short_pith_number":"pith:4FCZV5GE","schema_version":"1.0","canonical_sha256":"e1459af4c4c31c3e1c85c5db818ea6e39c62d8c8f1dcc83421195d4302487577","source":{"kind":"arxiv","id":"1908.03679","version":1},"attestation_state":"computed","paper":{"title":"Distance Map Loss Penalty Term for Semantic Segmentation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Alejandro Morales Martinez, Claudia Iriondo, Francesco Caliva, Sharmila Majumdar, Valentina Pedoia","submitted_at":"2019-08-10T03:37:18Z","abstract_excerpt":"Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morphology and shape features. In this work, we propose a novel distance map derived loss penalty term for semantic segmentation. We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions. We investigate the effects of this penalizing factor against cro"},"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":"1908.03679","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-10T03:37:18Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1bdfd5f1ad838b4ac329e5f06f461e8f3288623f0ac1052ef5307624b296832d","abstract_canon_sha256":"585d60df65add84fda2c2b8f99d039003edd63edc3935ec576a6402f66199d29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:53:17.187102Z","signature_b64":"Ehb/uK0o8+kkVoRTf8Blcsalk+i9ZQc2s/WK6lt9gvAlWqas6rhpyGiXq7/L9jUTaGLMQ10B9J4ZWzKN4NhPDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1459af4c4c31c3e1c85c5db818ea6e39c62d8c8f1dcc83421195d4302487577","last_reissued_at":"2026-07-04T23:53:17.186650Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:53:17.186650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distance Map Loss Penalty Term for Semantic Segmentation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Alejandro Morales Martinez, Claudia Iriondo, Francesco Caliva, Sharmila Majumdar, Valentina Pedoia","submitted_at":"2019-08-10T03:37:18Z","abstract_excerpt":"Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morphology and shape features. In this work, we propose a novel distance map derived loss penalty term for semantic segmentation. We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions. We investigate the effects of this penalizing factor against cro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03679","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/1908.03679/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":"1908.03679","created_at":"2026-07-04T23:53:17.186714+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.03679v1","created_at":"2026-07-04T23:53:17.186714+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03679","created_at":"2026-07-04T23:53:17.186714+00:00"},{"alias_kind":"pith_short_12","alias_value":"4FCZV5GEYMOD","created_at":"2026-07-04T23:53:17.186714+00:00"},{"alias_kind":"pith_short_16","alias_value":"4FCZV5GEYMOD4HEF","created_at":"2026-07-04T23:53:17.186714+00:00"},{"alias_kind":"pith_short_8","alias_value":"4FCZV5GE","created_at":"2026-07-04T23:53:17.186714+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14805","citing_title":"From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O","json":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O.json","graph_json":"https://pith.science/api/pith-number/4FCZV5GEYMOD4HEFYXNYDDVG4O/graph.json","events_json":"https://pith.science/api/pith-number/4FCZV5GEYMOD4HEFYXNYDDVG4O/events.json","paper":"https://pith.science/paper/4FCZV5GE"},"agent_actions":{"view_html":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O","download_json":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O.json","view_paper":"https://pith.science/paper/4FCZV5GE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.03679&json=true","fetch_graph":"https://pith.science/api/pith-number/4FCZV5GEYMOD4HEFYXNYDDVG4O/graph.json","fetch_events":"https://pith.science/api/pith-number/4FCZV5GEYMOD4HEFYXNYDDVG4O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O/action/storage_attestation","attest_author":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O/action/author_attestation","sign_citation":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O/action/citation_signature","submit_replication":"https://pith.science/pith/4FCZV5GEYMOD4HEFYXNYDDVG4O/action/replication_record"}},"created_at":"2026-07-04T23:53:17.186714+00:00","updated_at":"2026-07-04T23:53:17.186714+00:00"}