{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HHHKMTSKDMTX4NQZBIJERP5OKH","short_pith_number":"pith:HHHKMTSK","schema_version":"1.0","canonical_sha256":"39cea64e4a1b277e36190a1248bfae51f65d84ce990f5e4a9463b2b8a73a0cc9","source":{"kind":"arxiv","id":"1901.09263","version":1},"attestation_state":"computed","paper":{"title":"Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eytan Kats, Hayit Greenspan, Jacob Goldberger","submitted_at":"2019-01-26T18:52:46Z","abstract_excerpt":"This paper explores the use of a soft ground-truth mask (\"soft mask'') to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data - with a reduced confidence weight. A soft mask"},"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":"1901.09263","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-26T18:52:46Z","cross_cats_sorted":[],"title_canon_sha256":"7512e4581af886d86b52da9444336f8b407e52680c3b341dc92549687e3e09cf","abstract_canon_sha256":"1d956f43ad8d67dfad3c7f8625cedf9ab3437ba624bc1a0f1fb3250af44cbdba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:25.294033Z","signature_b64":"ugwAjD8GX+FzH3n23to97MSaCi1wiAUMlJjq4Jl4TF9F1jZszyE9k+tlutOMbw4ruJS9Yh5+ADamSupsowAyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39cea64e4a1b277e36190a1248bfae51f65d84ce990f5e4a9463b2b8a73a0cc9","last_reissued_at":"2026-05-17T23:55:25.293522Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:25.293522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eytan Kats, Hayit Greenspan, Jacob Goldberger","submitted_at":"2019-01-26T18:52:46Z","abstract_excerpt":"This paper explores the use of a soft ground-truth mask (\"soft mask'') to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data - with a reduced confidence weight. A soft mask"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09263","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":"1901.09263","created_at":"2026-05-17T23:55:25.293606+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09263v1","created_at":"2026-05-17T23:55:25.293606+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09263","created_at":"2026-05-17T23:55:25.293606+00:00"},{"alias_kind":"pith_short_12","alias_value":"HHHKMTSKDMTX","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HHHKMTSKDMTX4NQZ","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HHHKMTSK","created_at":"2026-05-18T12:33:18.533446+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/HHHKMTSKDMTX4NQZBIJERP5OKH","json":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH.json","graph_json":"https://pith.science/api/pith-number/HHHKMTSKDMTX4NQZBIJERP5OKH/graph.json","events_json":"https://pith.science/api/pith-number/HHHKMTSKDMTX4NQZBIJERP5OKH/events.json","paper":"https://pith.science/paper/HHHKMTSK"},"agent_actions":{"view_html":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH","download_json":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH.json","view_paper":"https://pith.science/paper/HHHKMTSK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09263&json=true","fetch_graph":"https://pith.science/api/pith-number/HHHKMTSKDMTX4NQZBIJERP5OKH/graph.json","fetch_events":"https://pith.science/api/pith-number/HHHKMTSKDMTX4NQZBIJERP5OKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH/action/storage_attestation","attest_author":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH/action/author_attestation","sign_citation":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH/action/citation_signature","submit_replication":"https://pith.science/pith/HHHKMTSKDMTX4NQZBIJERP5OKH/action/replication_record"}},"created_at":"2026-05-17T23:55:25.293606+00:00","updated_at":"2026-05-17T23:55:25.293606+00:00"}