{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:B67DP3UHO2WSACIA5EHPLEPUDU","short_pith_number":"pith:B67DP3UH","canonical_record":{"source":{"id":"2508.17768","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-25T08:06:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c465e9df5ef26b386ad562a13318753a7e55248bafe106b404a87b845f5f995d","abstract_canon_sha256":"d2954593a8f8243f1b958b0c938740082524db5cd8cceb4199a44b48a559f01b"},"schema_version":"1.0"},"canonical_sha256":"0fbe37ee8776ad200900e90ef591f41d1116efcb1ad312144aba5880f8ba07ae","source":{"kind":"arxiv","id":"2508.17768","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.17768","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2508.17768v1","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.17768","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"B67DP3UHO2WS","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"B67DP3UHO2WSACIA","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"B67DP3UH","created_at":"2026-07-05T11:58:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:B67DP3UHO2WSACIA5EHPLEPUDU","target":"record","payload":{"canonical_record":{"source":{"id":"2508.17768","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-25T08:06:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c465e9df5ef26b386ad562a13318753a7e55248bafe106b404a87b845f5f995d","abstract_canon_sha256":"d2954593a8f8243f1b958b0c938740082524db5cd8cceb4199a44b48a559f01b"},"schema_version":"1.0"},"canonical_sha256":"0fbe37ee8776ad200900e90ef591f41d1116efcb1ad312144aba5880f8ba07ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:47.406226Z","signature_b64":"NP8cnyu5cWdWLK9esFiMRd9+hPACSwFJQG50u4DVKjXRQ1Nn+FzfRPX6CvVukxSBIMjoWIhRWCx/eCJXu3ONAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fbe37ee8776ad200900e90ef591f41d1116efcb1ad312144aba5880f8ba07ae","last_reissued_at":"2026-07-05T11:58:47.405750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:47.405750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.17768","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MSm7AGIytE+KavtWL0kyu5Ka1bkCJBgIm9X5RUJA6WnTCkk96n6lB11CaJy+eOTAZCWkZ7VjpXLUW73tJzpHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:19:36.259125Z"},"content_sha256":"0f3069c91400325a803434c6ecd10d49ff52c8a57d8de3e5062126a708d69091","schema_version":"1.0","event_id":"sha256:0f3069c91400325a803434c6ecd10d49ff52c8a57d8de3e5062126a708d69091"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:B67DP3UHO2WSACIA5EHPLEPUDU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Adaobi Chiazor Emegoakor, Chinasa Kalaiwo, Confidence Raymond, Farouk Dako, Maimoona Akram, Maruf Adewole, Prince Ebenezer Adjei, Toufiq Musah, Ubaida Napari Abdulai, Udunna C. Anazodo","submitted_at":"2025-08-25T08:06:07Z","abstract_excerpt":"Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In this work, we evaluate the use of a modified Residual Encoder U-Net for breast ultrasound segmentation, with a focus on uncertainty quantification. We identify and correct for data duplication in the BUSI dataset, and use a deduplicated subset for more reliable estimates of generalization performance. Epistemic uncertainty is quantified using Monte Carlo dropout, deep ensembles, and their combination. Models are ben"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.17768","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/2508.17768/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":"2026-07-05T11:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4RCNO3sEoL9gptqr167RxOgm6jNEsNVnl5KKASSYLwNCIDg1zQy+8xzAEpPWt369sq5qVN0e4rFCim5IJtfHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:19:36.259523Z"},"content_sha256":"df4adecacd598812e60fe73a238200a7489f0e01d09f36b272f3473e98002786","schema_version":"1.0","event_id":"sha256:df4adecacd598812e60fe73a238200a7489f0e01d09f36b272f3473e98002786"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B67DP3UHO2WSACIA5EHPLEPUDU/bundle.json","state_url":"https://pith.science/pith/B67DP3UHO2WSACIA5EHPLEPUDU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B67DP3UHO2WSACIA5EHPLEPUDU/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":"2026-07-08T16:19:36Z","links":{"resolver":"https://pith.science/pith/B67DP3UHO2WSACIA5EHPLEPUDU","bundle":"https://pith.science/pith/B67DP3UHO2WSACIA5EHPLEPUDU/bundle.json","state":"https://pith.science/pith/B67DP3UHO2WSACIA5EHPLEPUDU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B67DP3UHO2WSACIA5EHPLEPUDU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:B67DP3UHO2WSACIA5EHPLEPUDU","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":"d2954593a8f8243f1b958b0c938740082524db5cd8cceb4199a44b48a559f01b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-25T08:06:07Z","title_canon_sha256":"c465e9df5ef26b386ad562a13318753a7e55248bafe106b404a87b845f5f995d"},"schema_version":"1.0","source":{"id":"2508.17768","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.17768","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2508.17768v1","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.17768","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"B67DP3UHO2WS","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"B67DP3UHO2WSACIA","created_at":"2026-07-05T11:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"B67DP3UH","created_at":"2026-07-05T11:58:47Z"}],"graph_snapshots":[{"event_id":"sha256:df4adecacd598812e60fe73a238200a7489f0e01d09f36b272f3473e98002786","target":"graph","created_at":"2026-07-05T11:58:47Z","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/2508.17768/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In this work, we evaluate the use of a modified Residual Encoder U-Net for breast ultrasound segmentation, with a focus on uncertainty quantification. We identify and correct for data duplication in the BUSI dataset, and use a deduplicated subset for more reliable estimates of generalization performance. Epistemic uncertainty is quantified using Monte Carlo dropout, deep ensembles, and their combination. Models are ben","authors_text":"Adaobi Chiazor Emegoakor, Chinasa Kalaiwo, Confidence Raymond, Farouk Dako, Maimoona Akram, Maruf Adewole, Prince Ebenezer Adjei, Toufiq Musah, Ubaida Napari Abdulai, Udunna C. Anazodo","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-25T08:06:07Z","title":"Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.17768","kind":"arxiv","version":1},"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":"sha256:0f3069c91400325a803434c6ecd10d49ff52c8a57d8de3e5062126a708d69091","target":"record","created_at":"2026-07-05T11:58:47Z","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":"d2954593a8f8243f1b958b0c938740082524db5cd8cceb4199a44b48a559f01b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-25T08:06:07Z","title_canon_sha256":"c465e9df5ef26b386ad562a13318753a7e55248bafe106b404a87b845f5f995d"},"schema_version":"1.0","source":{"id":"2508.17768","kind":"arxiv","version":1}},"canonical_sha256":"0fbe37ee8776ad200900e90ef591f41d1116efcb1ad312144aba5880f8ba07ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fbe37ee8776ad200900e90ef591f41d1116efcb1ad312144aba5880f8ba07ae","first_computed_at":"2026-07-05T11:58:47.405750Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:47.405750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NP8cnyu5cWdWLK9esFiMRd9+hPACSwFJQG50u4DVKjXRQ1Nn+FzfRPX6CvVukxSBIMjoWIhRWCx/eCJXu3ONAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:47.406226Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.17768","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f3069c91400325a803434c6ecd10d49ff52c8a57d8de3e5062126a708d69091","sha256:df4adecacd598812e60fe73a238200a7489f0e01d09f36b272f3473e98002786"],"state_sha256":"84f5f5532a8b3ead5df4727b89f42bb4635cf117b5b5fed6fae1de4188643844"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lJqhHp/Z2nZWWmjEIWuKKrwEVk9lok+lBh/4Hx0ZlOAo+MrB5b54kaqNOUtRPkwO7CJp0S9aHblXq3yaw9l4Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T16:19:36.261782Z","bundle_sha256":"0f0a231d47227cb1d67d62ee793dbc21dd70ef47d48a0af464287b0eef92afe7"}}