{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZSYWDKJVXVNAJII737U3RZS2SO","short_pith_number":"pith:ZSYWDKJV","schema_version":"1.0","canonical_sha256":"ccb161a935bd5a04a11fdfe9b8e65a9393f3c47e2b3191ad8a393685b466e943","source":{"kind":"arxiv","id":"2603.17576","version":3},"attestation_state":"computed","paper":{"title":"LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Melika Qahqaie, Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat","submitted_at":"2026-03-18T10:33:32Z","abstract_excerpt":"Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical decisions. To address this, we propose LoGSAM, a parameter-efficient, detection-driven framework that transforms radiologist dictation into text prompts for foundation-model-based localization and segmentation. Radiologist speech is first transcribed and translated using a pretrained Whisper ASR model, followed by negation-aware clinical NLP to extract tumor-specific textual prompts. These prompts guide text-conditioned tumor localization via a LoR"},"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":"2603.17576","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-18T10:33:32Z","cross_cats_sorted":[],"title_canon_sha256":"363784852e2cbbb20d421c1568668e079fd8aa0eaad220f6c42cb3afe8f1d778","abstract_canon_sha256":"c1980c143776a0f15b04a5e3addd0d44cad7705e4c4d15f9e7368a3f7294e52d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:36.900157Z","signature_b64":"C6fEgVtLPvsWeOM2uwvZ4Xg3/0qpBYFzut33pzvx6dJ+0AoLIUVGabqpRHqvv+Yxds/rVbpoAOXops4tO8iiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccb161a935bd5a04a11fdfe9b8e65a9393f3c47e2b3191ad8a393685b466e943","last_reissued_at":"2026-06-30T01:17:36.899338Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:36.899338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Melika Qahqaie, Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat","submitted_at":"2026-03-18T10:33:32Z","abstract_excerpt":"Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical decisions. To address this, we propose LoGSAM, a parameter-efficient, detection-driven framework that transforms radiologist dictation into text prompts for foundation-model-based localization and segmentation. Radiologist speech is first transcribed and translated using a pretrained Whisper ASR model, followed by negation-aware clinical NLP to extract tumor-specific textual prompts. These prompts guide text-conditioned tumor localization via a LoR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.17576","kind":"arxiv","version":3},"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/2603.17576/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":"2603.17576","created_at":"2026-06-30T01:17:36.899438+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.17576v3","created_at":"2026-06-30T01:17:36.899438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.17576","created_at":"2026-06-30T01:17:36.899438+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZSYWDKJVXVNA","created_at":"2026-06-30T01:17:36.899438+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZSYWDKJVXVNAJII7","created_at":"2026-06-30T01:17:36.899438+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZSYWDKJV","created_at":"2026-06-30T01:17:36.899438+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/ZSYWDKJVXVNAJII737U3RZS2SO","json":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO.json","graph_json":"https://pith.science/api/pith-number/ZSYWDKJVXVNAJII737U3RZS2SO/graph.json","events_json":"https://pith.science/api/pith-number/ZSYWDKJVXVNAJII737U3RZS2SO/events.json","paper":"https://pith.science/paper/ZSYWDKJV"},"agent_actions":{"view_html":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO","download_json":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO.json","view_paper":"https://pith.science/paper/ZSYWDKJV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.17576&json=true","fetch_graph":"https://pith.science/api/pith-number/ZSYWDKJVXVNAJII737U3RZS2SO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZSYWDKJVXVNAJII737U3RZS2SO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO/action/storage_attestation","attest_author":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO/action/author_attestation","sign_citation":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO/action/citation_signature","submit_replication":"https://pith.science/pith/ZSYWDKJVXVNAJII737U3RZS2SO/action/replication_record"}},"created_at":"2026-06-30T01:17:36.899438+00:00","updated_at":"2026-06-30T01:17:36.899438+00:00"}