{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YAPLU3FRCKAG22WFSGCD7K2ISN","short_pith_number":"pith:YAPLU3FR","schema_version":"1.0","canonical_sha256":"c01eba6cb112806d6ac591843fab48936050777b1c25073bf7c45877517a0993","source":{"kind":"arxiv","id":"1906.01891","version":4},"attestation_state":"computed","paper":{"title":"Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Florian Dubost, Gerda Bortsova, Gijs van Tulder, Hieab Adams, M. Arfan Ikram, Marleen de Bruijne, Meike Vernooij, Pinar Yilmaz, Wiro Niessen","submitted_at":"2019-06-05T09:08:38Z","abstract_excerpt":"Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions. These attention maps are computed using the last feature maps of a segmentation network optimized only with global image-level labels. The proposed method can ge"},"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":"1906.01891","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T09:08:38Z","cross_cats_sorted":[],"title_canon_sha256":"9d1500e80f06e48a58aea9b36abb32fe249157391b18d821214ac3823462669f","abstract_canon_sha256":"196bde50fcdea4f3c6c42e14577deb302e62b3b35ac622cc5f90656e142ffe94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:23.637034Z","signature_b64":"fypzQkGLzUT6at4vcspmyccmop4pvOEd/eH9lK10aK6aBSXuTU9x8AW8x04h3r9zqXc0+qRw0uCm38AjmBHJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c01eba6cb112806d6ac591843fab48936050777b1c25073bf7c45877517a0993","last_reissued_at":"2026-07-05T00:49:23.636587Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:23.636587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Florian Dubost, Gerda Bortsova, Gijs van Tulder, Hieab Adams, M. Arfan Ikram, Marleen de Bruijne, Meike Vernooij, Pinar Yilmaz, Wiro Niessen","submitted_at":"2019-06-05T09:08:38Z","abstract_excerpt":"Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions. These attention maps are computed using the last feature maps of a segmentation network optimized only with global image-level labels. The proposed method can ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01891","kind":"arxiv","version":4},"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/1906.01891/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":"1906.01891","created_at":"2026-07-05T00:49:23.636642+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01891v4","created_at":"2026-07-05T00:49:23.636642+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01891","created_at":"2026-07-05T00:49:23.636642+00:00"},{"alias_kind":"pith_short_12","alias_value":"YAPLU3FRCKAG","created_at":"2026-07-05T00:49:23.636642+00:00"},{"alias_kind":"pith_short_16","alias_value":"YAPLU3FRCKAG22WF","created_at":"2026-07-05T00:49:23.636642+00:00"},{"alias_kind":"pith_short_8","alias_value":"YAPLU3FR","created_at":"2026-07-05T00:49:23.636642+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/YAPLU3FRCKAG22WFSGCD7K2ISN","json":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN.json","graph_json":"https://pith.science/api/pith-number/YAPLU3FRCKAG22WFSGCD7K2ISN/graph.json","events_json":"https://pith.science/api/pith-number/YAPLU3FRCKAG22WFSGCD7K2ISN/events.json","paper":"https://pith.science/paper/YAPLU3FR"},"agent_actions":{"view_html":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN","download_json":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN.json","view_paper":"https://pith.science/paper/YAPLU3FR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01891&json=true","fetch_graph":"https://pith.science/api/pith-number/YAPLU3FRCKAG22WFSGCD7K2ISN/graph.json","fetch_events":"https://pith.science/api/pith-number/YAPLU3FRCKAG22WFSGCD7K2ISN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN/action/storage_attestation","attest_author":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN/action/author_attestation","sign_citation":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN/action/citation_signature","submit_replication":"https://pith.science/pith/YAPLU3FRCKAG22WFSGCD7K2ISN/action/replication_record"}},"created_at":"2026-07-05T00:49:23.636642+00:00","updated_at":"2026-07-05T00:49:23.636642+00:00"}