{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3MLNTLOE56VKSV54SV4JANJBDR","short_pith_number":"pith:3MLNTLOE","canonical_record":{"source":{"id":"2505.05573","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T18:07:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c4e98ee15458ec722e0ee4e3b62083ce983c319f5fd0ad0a4169f73d2e4dc350","abstract_canon_sha256":"85e38b31390faecbf9cc3c9f734e393abd31892ec18df8fda7624660f3b84415"},"schema_version":"1.0"},"canonical_sha256":"db16d9adc4efaaa957bc95789035211c44cb59b31f2110ecf45affe90834388f","source":{"kind":"arxiv","id":"2505.05573","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.05573","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"arxiv_version","alias_value":"2505.05573v2","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05573","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_12","alias_value":"3MLNTLOE56VK","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_16","alias_value":"3MLNTLOE56VKSV54","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_8","alias_value":"3MLNTLOE","created_at":"2026-07-05T11:01:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3MLNTLOE56VKSV54SV4JANJBDR","target":"record","payload":{"canonical_record":{"source":{"id":"2505.05573","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T18:07:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c4e98ee15458ec722e0ee4e3b62083ce983c319f5fd0ad0a4169f73d2e4dc350","abstract_canon_sha256":"85e38b31390faecbf9cc3c9f734e393abd31892ec18df8fda7624660f3b84415"},"schema_version":"1.0"},"canonical_sha256":"db16d9adc4efaaa957bc95789035211c44cb59b31f2110ecf45affe90834388f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:37.001608Z","signature_b64":"T6PBABnHIeEPP6y8hQQR/bxyf7qCfR1OFNisr3k1//dA3nnozGFZw07T9IXMvyi9xz90UltI1QXlzIt+JI1VCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db16d9adc4efaaa957bc95789035211c44cb59b31f2110ecf45affe90834388f","last_reissued_at":"2026-07-05T11:01:37.001118Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:37.001118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.05573","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:01:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iCaB02ZBxYVIStZ3aAP1ZBcAkM/bR0ZDe7inu9bFAZifZ8NgdsOcR368jkzIPLGLp3laoj/6xkYmT/aFdAqUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:57:08.248942Z"},"content_sha256":"1d467781888c26ea79500a2eae8c9cd82c4a9f54364a8ee8ea36e7b245d3d94e","schema_version":"1.0","event_id":"sha256:1d467781888c26ea79500a2eae8c9cd82c4a9f54364a8ee8ea36e7b245d3d94e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3MLNTLOE56VKSV54SV4JANJBDR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Elena Tutubalina, Mikhail Chaichuk, Steven Hicks, Sushant Gautam","submitted_at":"2025-05-08T18:07:16Z","abstract_excerpt":"The generation of realistic medical images from text descriptions has significant potential to address data scarcity challenges in healthcare AI while preserving patient privacy. This paper presents a comprehensive study of text-to-image synthesis in the medical domain, comparing two distinct approaches: (1) fine-tuning large pre-trained latent diffusion models and (2) training small, domain-specific models. We introduce a novel model named MSDM, an optimized architecture based on Stable Diffusion that integrates a clinical text encoder, variational autoencoder, and cross-attention mechanisms "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05573","kind":"arxiv","version":2},"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/2505.05573/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:01:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sjp2sSXZmgY9ueR96/r1fZ2TpqefikEYEh3+y6htnFT3awhzpSjA6tobb1ze5Gar40604DQ1WB5750lUsVzJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:57:08.249438Z"},"content_sha256":"a2b80d3d07815c7cb863616e452009c907d9b410d957a0edc99181e5a46c3b11","schema_version":"1.0","event_id":"sha256:a2b80d3d07815c7cb863616e452009c907d9b410d957a0edc99181e5a46c3b11"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3MLNTLOE56VKSV54SV4JANJBDR/bundle.json","state_url":"https://pith.science/pith/3MLNTLOE56VKSV54SV4JANJBDR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3MLNTLOE56VKSV54SV4JANJBDR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T18:57:08Z","links":{"resolver":"https://pith.science/pith/3MLNTLOE56VKSV54SV4JANJBDR","bundle":"https://pith.science/pith/3MLNTLOE56VKSV54SV4JANJBDR/bundle.json","state":"https://pith.science/pith/3MLNTLOE56VKSV54SV4JANJBDR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3MLNTLOE56VKSV54SV4JANJBDR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3MLNTLOE56VKSV54SV4JANJBDR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"85e38b31390faecbf9cc3c9f734e393abd31892ec18df8fda7624660f3b84415","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T18:07:16Z","title_canon_sha256":"c4e98ee15458ec722e0ee4e3b62083ce983c319f5fd0ad0a4169f73d2e4dc350"},"schema_version":"1.0","source":{"id":"2505.05573","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.05573","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"arxiv_version","alias_value":"2505.05573v2","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05573","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_12","alias_value":"3MLNTLOE56VK","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_16","alias_value":"3MLNTLOE56VKSV54","created_at":"2026-07-05T11:01:37Z"},{"alias_kind":"pith_short_8","alias_value":"3MLNTLOE","created_at":"2026-07-05T11:01:37Z"}],"graph_snapshots":[{"event_id":"sha256:a2b80d3d07815c7cb863616e452009c907d9b410d957a0edc99181e5a46c3b11","target":"graph","created_at":"2026-07-05T11:01:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.05573/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The generation of realistic medical images from text descriptions has significant potential to address data scarcity challenges in healthcare AI while preserving patient privacy. This paper presents a comprehensive study of text-to-image synthesis in the medical domain, comparing two distinct approaches: (1) fine-tuning large pre-trained latent diffusion models and (2) training small, domain-specific models. We introduce a novel model named MSDM, an optimized architecture based on Stable Diffusion that integrates a clinical text encoder, variational autoencoder, and cross-attention mechanisms ","authors_text":"Elena Tutubalina, Mikhail Chaichuk, Steven Hicks, Sushant Gautam","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T18:07:16Z","title":"Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05573","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1d467781888c26ea79500a2eae8c9cd82c4a9f54364a8ee8ea36e7b245d3d94e","target":"record","created_at":"2026-07-05T11:01:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"85e38b31390faecbf9cc3c9f734e393abd31892ec18df8fda7624660f3b84415","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T18:07:16Z","title_canon_sha256":"c4e98ee15458ec722e0ee4e3b62083ce983c319f5fd0ad0a4169f73d2e4dc350"},"schema_version":"1.0","source":{"id":"2505.05573","kind":"arxiv","version":2}},"canonical_sha256":"db16d9adc4efaaa957bc95789035211c44cb59b31f2110ecf45affe90834388f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db16d9adc4efaaa957bc95789035211c44cb59b31f2110ecf45affe90834388f","first_computed_at":"2026-07-05T11:01:37.001118Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:01:37.001118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T6PBABnHIeEPP6y8hQQR/bxyf7qCfR1OFNisr3k1//dA3nnozGFZw07T9IXMvyi9xz90UltI1QXlzIt+JI1VCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:01:37.001608Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.05573","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d467781888c26ea79500a2eae8c9cd82c4a9f54364a8ee8ea36e7b245d3d94e","sha256:a2b80d3d07815c7cb863616e452009c907d9b410d957a0edc99181e5a46c3b11"],"state_sha256":"c7b899209850b471fd310e8e308373e0cbd176d1add5b7d808ffe64664687e07"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+srv8YL6tCg05Mub/ncH48Jc6UKiB12dX8oums/Z85UXjnnQ4nY/CWeGq1UIDQe6yenJceh6mTm2dolOJv7gDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T18:57:08.253078Z","bundle_sha256":"d98705b26be16dcce1750a3c857a4bff237a3bb402c6e98201826c1ca37dd1e9"}}