{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RC4F6OZTGRZ6TDBT76NCW2BG3G","short_pith_number":"pith:RC4F6OZT","schema_version":"1.0","canonical_sha256":"88b85f3b333473e98c33ff9a2b6826d9827574603a8373966f59309645c15f5c","source":{"kind":"arxiv","id":"2403.08796","version":1},"attestation_state":"computed","paper":{"title":"Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"eess.IV","authors_text":"Hadjer Benmeziane, Imane Hamzaoui, Kaoutar El Maghraoui, Zayneb Cherif","submitted_at":"2024-02-01T17:25:14Z","abstract_excerpt":"This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge. It contrasts AIMC's efficiency with traditional digital computing's limitations in power, speed, and scalability. Our comprehensive evaluation focuses on brain tumor analysis, spleen segmentation, and nuclei detection. The study highlights the superior robustness of isotropic architectures, which exhibit a minimal accuracy drop (0.04) in analog-aware training, compared to significant drops (up to 0.15) in pyramidal s"},"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":"2403.08796","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-02-01T17:25:14Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"f32b502e26d869dc770a016386641e409030be0090a45599750403f9e2b5d9dd","abstract_canon_sha256":"7afa0e81cac5c2fb2d37db71f061b9c25027c530f3a71d9cddc35bc82e718438"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:47.152458Z","signature_b64":"gsy3foTk9m2CqtxVbAZQmbfa9vNrFn+2lsaZ/IcAbBTOUTx8e5SrjubC5kqwrmY5JXT9kFsWNwIidAxab36XDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88b85f3b333473e98c33ff9a2b6826d9827574603a8373966f59309645c15f5c","last_reissued_at":"2026-07-05T07:55:47.152042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:47.152042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"eess.IV","authors_text":"Hadjer Benmeziane, Imane Hamzaoui, Kaoutar El Maghraoui, Zayneb Cherif","submitted_at":"2024-02-01T17:25:14Z","abstract_excerpt":"This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge. It contrasts AIMC's efficiency with traditional digital computing's limitations in power, speed, and scalability. Our comprehensive evaluation focuses on brain tumor analysis, spleen segmentation, and nuclei detection. The study highlights the superior robustness of isotropic architectures, which exhibit a minimal accuracy drop (0.04) in analog-aware training, compared to significant drops (up to 0.15) in pyramidal s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.08796","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/2403.08796/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":"2403.08796","created_at":"2026-07-05T07:55:47.152106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.08796v1","created_at":"2026-07-05T07:55:47.152106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.08796","created_at":"2026-07-05T07:55:47.152106+00:00"},{"alias_kind":"pith_short_12","alias_value":"RC4F6OZTGRZ6","created_at":"2026-07-05T07:55:47.152106+00:00"},{"alias_kind":"pith_short_16","alias_value":"RC4F6OZTGRZ6TDBT","created_at":"2026-07-05T07:55:47.152106+00:00"},{"alias_kind":"pith_short_8","alias_value":"RC4F6OZT","created_at":"2026-07-05T07:55:47.152106+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/RC4F6OZTGRZ6TDBT76NCW2BG3G","json":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G.json","graph_json":"https://pith.science/api/pith-number/RC4F6OZTGRZ6TDBT76NCW2BG3G/graph.json","events_json":"https://pith.science/api/pith-number/RC4F6OZTGRZ6TDBT76NCW2BG3G/events.json","paper":"https://pith.science/paper/RC4F6OZT"},"agent_actions":{"view_html":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G","download_json":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G.json","view_paper":"https://pith.science/paper/RC4F6OZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.08796&json=true","fetch_graph":"https://pith.science/api/pith-number/RC4F6OZTGRZ6TDBT76NCW2BG3G/graph.json","fetch_events":"https://pith.science/api/pith-number/RC4F6OZTGRZ6TDBT76NCW2BG3G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G/action/storage_attestation","attest_author":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G/action/author_attestation","sign_citation":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G/action/citation_signature","submit_replication":"https://pith.science/pith/RC4F6OZTGRZ6TDBT76NCW2BG3G/action/replication_record"}},"created_at":"2026-07-05T07:55:47.152106+00:00","updated_at":"2026-07-05T07:55:47.152106+00:00"}