{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EUDVUUJRQNCQ24SDUBRTDYX3AJ","short_pith_number":"pith:EUDVUUJR","schema_version":"1.0","canonical_sha256":"25075a513183450d7243a06331e2fb024779ea9f06bba39bb181ebd11f6f7d03","source":{"kind":"arxiv","id":"2210.16273","version":1},"attestation_state":"computed","paper":{"title":"SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"cs.CV","authors_text":"Alexander M\\\"uhlberg, Andreas K. Maier, Chlo\\\"e Goossens, Dominik N\\\"orenberg, Dominik Schneidereit, Felix Denzinger, Lucas Kreiss, Michael Haug, Oliver Friedrich, Oliver Taubmann, Paul Ritter, Simon Langer, Stefanie N\\\"ubler, Wolfgang H. Goldmann","submitted_at":"2022-10-28T17:03:04Z","abstract_excerpt":"Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biolo"},"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":"2210.16273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T17:03:04Z","cross_cats_sorted":["cs.LG","q-bio.QM"],"title_canon_sha256":"d3643ce2658a2854763b4766a96ec39c62d6127f5740b7437cb049e771549c21","abstract_canon_sha256":"f648ad9ae6664e338c34ed4818d6c9bd0a261755d8fed4f43819423d22d0830f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:32.450988Z","signature_b64":"E03RTDO8Bc3ecQDfmZZxy41HPya8K5wI5Wgq3x7/woV3SmiAg7xCQialE1y66F210klOLCiY3lG/tbpT57grCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25075a513183450d7243a06331e2fb024779ea9f06bba39bb181ebd11f6f7d03","last_reissued_at":"2026-07-05T05:11:32.450562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:32.450562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"cs.CV","authors_text":"Alexander M\\\"uhlberg, Andreas K. Maier, Chlo\\\"e Goossens, Dominik N\\\"orenberg, Dominik Schneidereit, Felix Denzinger, Lucas Kreiss, Michael Haug, Oliver Friedrich, Oliver Taubmann, Paul Ritter, Simon Langer, Stefanie N\\\"ubler, Wolfgang H. Goldmann","submitted_at":"2022-10-28T17:03:04Z","abstract_excerpt":"Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16273","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/2210.16273/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":"2210.16273","created_at":"2026-07-05T05:11:32.450627+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.16273v1","created_at":"2026-07-05T05:11:32.450627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16273","created_at":"2026-07-05T05:11:32.450627+00:00"},{"alias_kind":"pith_short_12","alias_value":"EUDVUUJRQNCQ","created_at":"2026-07-05T05:11:32.450627+00:00"},{"alias_kind":"pith_short_16","alias_value":"EUDVUUJRQNCQ24SD","created_at":"2026-07-05T05:11:32.450627+00:00"},{"alias_kind":"pith_short_8","alias_value":"EUDVUUJR","created_at":"2026-07-05T05:11:32.450627+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/EUDVUUJRQNCQ24SDUBRTDYX3AJ","json":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ.json","graph_json":"https://pith.science/api/pith-number/EUDVUUJRQNCQ24SDUBRTDYX3AJ/graph.json","events_json":"https://pith.science/api/pith-number/EUDVUUJRQNCQ24SDUBRTDYX3AJ/events.json","paper":"https://pith.science/paper/EUDVUUJR"},"agent_actions":{"view_html":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ","download_json":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ.json","view_paper":"https://pith.science/paper/EUDVUUJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.16273&json=true","fetch_graph":"https://pith.science/api/pith-number/EUDVUUJRQNCQ24SDUBRTDYX3AJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EUDVUUJRQNCQ24SDUBRTDYX3AJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ/action/storage_attestation","attest_author":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ/action/author_attestation","sign_citation":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ/action/citation_signature","submit_replication":"https://pith.science/pith/EUDVUUJRQNCQ24SDUBRTDYX3AJ/action/replication_record"}},"created_at":"2026-07-05T05:11:32.450627+00:00","updated_at":"2026-07-05T05:11:32.450627+00:00"}