{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VNAIEQS4CXCMKXP5YEYONMAVOV","short_pith_number":"pith:VNAIEQS4","canonical_record":{"source":{"id":"2404.08201","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-12T02:14:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"93520c56957737d6d024dcea13dcf420fbc556e3804f4da04820d80d4fd0388a","abstract_canon_sha256":"6ba4fa520cd4879b7aec61287b5884af9cbec77fc682ea4e057d173398aa5fb7"},"schema_version":"1.0"},"canonical_sha256":"ab4082425c15c4c55dfdc130e6b01575740a248ea8a3ea0ef63236f57b1c52d4","source":{"kind":"arxiv","id":"2404.08201","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.08201","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"arxiv_version","alias_value":"2404.08201v1","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.08201","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_12","alias_value":"VNAIEQS4CXCM","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_16","alias_value":"VNAIEQS4CXCMKXP5","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_8","alias_value":"VNAIEQS4","created_at":"2026-07-05T08:07:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VNAIEQS4CXCMKXP5YEYONMAVOV","target":"record","payload":{"canonical_record":{"source":{"id":"2404.08201","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-12T02:14:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"93520c56957737d6d024dcea13dcf420fbc556e3804f4da04820d80d4fd0388a","abstract_canon_sha256":"6ba4fa520cd4879b7aec61287b5884af9cbec77fc682ea4e057d173398aa5fb7"},"schema_version":"1.0"},"canonical_sha256":"ab4082425c15c4c55dfdc130e6b01575740a248ea8a3ea0ef63236f57b1c52d4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:19.193907Z","signature_b64":"e5/YuF2pqK0mc1epJgp2p+iwUz80CjtO4siv0iylVO1MhK8o6uHkUxZLc1NdUKnfsPhlRZ/Ajn9ZwLQrOUhQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab4082425c15c4c55dfdc130e6b01575740a248ea8a3ea0ef63236f57b1c52d4","last_reissued_at":"2026-07-05T08:07:19.193538Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:19.193538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.08201","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":"2026-07-05T08:07:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IqKmEmJNB3Dqkj6Na3nhWPhsHvYRQrrb6OZUifiNGE5sZ9QTVsFqvEQgcP+YlducCuv7/d5QoH9z6uu5oSLgDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-24T02:39:15.302286Z"},"content_sha256":"c7bf03e03c8d9e22c8e8ac462d8270ac5c94c8872fdb323073d341a065262ee1","schema_version":"1.0","event_id":"sha256:c7bf03e03c8d9e22c8e8ac462d8270ac5c94c8872fdb323073d341a065262ee1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VNAIEQS4CXCMKXP5YEYONMAVOV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Guanqun Sun, Junyi Xin, Le-Minh Nguyen, Teeradaj Racharak, Tianhua Yang, Yizhi Pan","submitted_at":"2024-04-12T02:14:35Z","abstract_excerpt":"In medical imaging, accurate image segmentation is crucial for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods lack an in-depth integration of global and local features, failing to pay special attention to abnormal regions and boundary details in medical images. To this end, we present a novel deep learning-based approach, MIPC-Net, for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, features two distinct modules: (i) \\textbf{Mutual Inclusion of Position and Channel Attention (MIP"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.08201","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/2404.08201/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":"2026-07-05T08:07:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pqcQbdttVahAqh+TJ6sQCHTc51u3LwF3iwoOXEgQechnq88Q33n9FtkTEaLvNEmnKevDbew7RFx67xrhWLhGBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-24T02:39:15.302794Z"},"content_sha256":"8f349e7378170d34f6d49c285dd77f0afbf33aa70f7aec148d99f0962efdfd9b","schema_version":"1.0","event_id":"sha256:8f349e7378170d34f6d49c285dd77f0afbf33aa70f7aec148d99f0962efdfd9b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/bundle.json","state_url":"https://pith.science/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/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":"2026-08-24T02:39:15Z","links":{"resolver":"https://pith.science/pith/VNAIEQS4CXCMKXP5YEYONMAVOV","bundle":"https://pith.science/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/bundle.json","state":"https://pith.science/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VNAIEQS4CXCMKXP5YEYONMAVOV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VNAIEQS4CXCMKXP5YEYONMAVOV","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":"6ba4fa520cd4879b7aec61287b5884af9cbec77fc682ea4e057d173398aa5fb7","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-12T02:14:35Z","title_canon_sha256":"93520c56957737d6d024dcea13dcf420fbc556e3804f4da04820d80d4fd0388a"},"schema_version":"1.0","source":{"id":"2404.08201","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.08201","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"arxiv_version","alias_value":"2404.08201v1","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.08201","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_12","alias_value":"VNAIEQS4CXCM","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_16","alias_value":"VNAIEQS4CXCMKXP5","created_at":"2026-07-05T08:07:19Z"},{"alias_kind":"pith_short_8","alias_value":"VNAIEQS4","created_at":"2026-07-05T08:07:19Z"}],"graph_snapshots":[{"event_id":"sha256:8f349e7378170d34f6d49c285dd77f0afbf33aa70f7aec148d99f0962efdfd9b","target":"graph","created_at":"2026-07-05T08:07:19Z","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/2404.08201/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In medical imaging, accurate image segmentation is crucial for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods lack an in-depth integration of global and local features, failing to pay special attention to abnormal regions and boundary details in medical images. To this end, we present a novel deep learning-based approach, MIPC-Net, for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, features two distinct modules: (i) \\textbf{Mutual Inclusion of Position and Channel Attention (MIP","authors_text":"Guanqun Sun, Junyi Xin, Le-Minh Nguyen, Teeradaj Racharak, Tianhua Yang, Yizhi Pan","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-12T02:14:35Z","title":"A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.08201","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:c7bf03e03c8d9e22c8e8ac462d8270ac5c94c8872fdb323073d341a065262ee1","target":"record","created_at":"2026-07-05T08:07:19Z","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":"6ba4fa520cd4879b7aec61287b5884af9cbec77fc682ea4e057d173398aa5fb7","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-12T02:14:35Z","title_canon_sha256":"93520c56957737d6d024dcea13dcf420fbc556e3804f4da04820d80d4fd0388a"},"schema_version":"1.0","source":{"id":"2404.08201","kind":"arxiv","version":1}},"canonical_sha256":"ab4082425c15c4c55dfdc130e6b01575740a248ea8a3ea0ef63236f57b1c52d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab4082425c15c4c55dfdc130e6b01575740a248ea8a3ea0ef63236f57b1c52d4","first_computed_at":"2026-07-05T08:07:19.193538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:07:19.193538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e5/YuF2pqK0mc1epJgp2p+iwUz80CjtO4siv0iylVO1MhK8o6uHkUxZLc1NdUKnfsPhlRZ/Ajn9ZwLQrOUhQBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:07:19.193907Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.08201","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7bf03e03c8d9e22c8e8ac462d8270ac5c94c8872fdb323073d341a065262ee1","sha256:8f349e7378170d34f6d49c285dd77f0afbf33aa70f7aec148d99f0962efdfd9b"],"state_sha256":"69408404da5ceed8cc6a31a67fd1d6ebff7cc7acabcc7c0491aa9f5d6526f3f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZZMXHLLUPMiXUHPPQ478XEoq62ozu6zrcbu7Bp6TNu0ewxZ55G0jByiYmTmWmtQK4Vvj1QQ5UAbodikFcTGcAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-24T02:39:15.307199Z","bundle_sha256":"d2b2faf1357642b73ebf1b454ca39208f26103e6f5b394f9439f28cb922758bf"}}