{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FSLE4KJ2KOIXAWHWM7KK2D6I5C","short_pith_number":"pith:FSLE4KJ2","schema_version":"1.0","canonical_sha256":"2c964e293a53917058f667d4ad0fc8e8a20f34a2c108fabe66f8013868f97b5a","source":{"kind":"arxiv","id":"1711.11069","version":1},"attestation_state":"computed","paper":{"title":"Detection-aided liver lesion segmentation using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jordi Pont-Tuset, Jordi Torres, Kevis-Kokitsi Maninis, Luc Van Gool, Miriam Bellver, Xavier Giro-i-Nieto","submitted_at":"2017-11-29T19:27:40Z","abstract_excerpt":"A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesion"},"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":"1711.11069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-29T19:27:40Z","cross_cats_sorted":[],"title_canon_sha256":"1f1926e029c72b2058e8992aaf61c11bdfaa22c01a8acdab7f0974ac6b03c366","abstract_canon_sha256":"ebcfba3b4d5fbf3f4c17edd8a2ec237eac176d0c59fdeed29820ce34a91491bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:12.581472Z","signature_b64":"GeadU17v/v52lR1d+PejbcsvDCYL76YBZRFVBhYliuI2otI03AkIl4V2ru4vUA/BiuIrKzgQVlDhYIpfY5MPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c964e293a53917058f667d4ad0fc8e8a20f34a2c108fabe66f8013868f97b5a","last_reissued_at":"2026-05-18T00:29:12.580784Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:12.580784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detection-aided liver lesion segmentation using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jordi Pont-Tuset, Jordi Torres, Kevis-Kokitsi Maninis, Luc Van Gool, Miriam Bellver, Xavier Giro-i-Nieto","submitted_at":"2017-11-29T19:27:40Z","abstract_excerpt":"A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11069","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":"1711.11069","created_at":"2026-05-18T00:29:12.580892+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.11069v1","created_at":"2026-05-18T00:29:12.580892+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11069","created_at":"2026-05-18T00:29:12.580892+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSLE4KJ2KOIX","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSLE4KJ2KOIXAWHW","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSLE4KJ2","created_at":"2026-05-18T12:31:15.632608+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/FSLE4KJ2KOIXAWHWM7KK2D6I5C","json":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C.json","graph_json":"https://pith.science/api/pith-number/FSLE4KJ2KOIXAWHWM7KK2D6I5C/graph.json","events_json":"https://pith.science/api/pith-number/FSLE4KJ2KOIXAWHWM7KK2D6I5C/events.json","paper":"https://pith.science/paper/FSLE4KJ2"},"agent_actions":{"view_html":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C","download_json":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C.json","view_paper":"https://pith.science/paper/FSLE4KJ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.11069&json=true","fetch_graph":"https://pith.science/api/pith-number/FSLE4KJ2KOIXAWHWM7KK2D6I5C/graph.json","fetch_events":"https://pith.science/api/pith-number/FSLE4KJ2KOIXAWHWM7KK2D6I5C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C/action/storage_attestation","attest_author":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C/action/author_attestation","sign_citation":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C/action/citation_signature","submit_replication":"https://pith.science/pith/FSLE4KJ2KOIXAWHWM7KK2D6I5C/action/replication_record"}},"created_at":"2026-05-18T00:29:12.580892+00:00","updated_at":"2026-05-18T00:29:12.580892+00:00"}