{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:36MJTPD42THAPVKTPURC4BEKTO","short_pith_number":"pith:36MJTPD4","canonical_record":{"source":{"id":"1903.11834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T08:39:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e11c876e0ab3582420d25c298bd7697b9444f0bb621634c5f571a4bc224c44e7","abstract_canon_sha256":"f18903020e0aea2ebc5c79808ea68b67cc0f2dcc0685f7f6601672b29fdbd8be"},"schema_version":"1.0"},"canonical_sha256":"df9899bc7cd4ce07d5537d222e048a9b8bf664513e29cacab69ff30a171e6c9b","source":{"kind":"arxiv","id":"1903.11834","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11834","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11834v1","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11834","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"36MJTPD42THA","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"36MJTPD42THAPVKT","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"36MJTPD4","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:36MJTPD42THAPVKTPURC4BEKTO","target":"record","payload":{"canonical_record":{"source":{"id":"1903.11834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T08:39:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e11c876e0ab3582420d25c298bd7697b9444f0bb621634c5f571a4bc224c44e7","abstract_canon_sha256":"f18903020e0aea2ebc5c79808ea68b67cc0f2dcc0685f7f6601672b29fdbd8be"},"schema_version":"1.0"},"canonical_sha256":"df9899bc7cd4ce07d5537d222e048a9b8bf664513e29cacab69ff30a171e6c9b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:58.969845Z","signature_b64":"3e4qteafY3m0AiopJQmjtlEminfYJY2nv9rwmBkjcjbSOYqj3r5nXUUEaGl2A5pVzH8mkG8Wt7557qBA7+i+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df9899bc7cd4ce07d5537d222e048a9b8bf664513e29cacab69ff30a171e6c9b","last_reissued_at":"2026-05-17T23:49:58.969409Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:58.969409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.11834","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":"2026-05-17T23:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KbehtqOO/1VrWaolupMUQVwwpqi3p1LQDigFlMNrY8aEalfCbvOJBYThjp7TDhkRvubkf8apRr02cBtdbCkHBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:59:54.147048Z"},"content_sha256":"fb9f73bd67424be8e7817595d4b3444d10284b592e5e6b3d0d2995e6488edadd","schema_version":"1.0","event_id":"sha256:fb9f73bd67424be8e7817595d4b3444d10284b592e5e6b3d0d2995e6488edadd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:36MJTPD42THAPVKTPURC4BEKTO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Pingkun Yan, Rong Zhang, Xueying Chen","submitted_at":"2019-03-28T08:39:49Z","abstract_excerpt":"Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11834","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":""},"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":"2026-05-17T23:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BS/BhBbWS1OiLns+22SHdWOR8DUtRCB6q0A54AcUTkek+gIeFTnRhDrFLxfO54RbLbCTcjgBBDg4bsn/ZSgrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:59:54.147736Z"},"content_sha256":"b8a226bc80061bf94932e5721474cd33f65d866d8d8a7153c7c8641e05dd6715","schema_version":"1.0","event_id":"sha256:b8a226bc80061bf94932e5721474cd33f65d866d8d8a7153c7c8641e05dd6715"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/36MJTPD42THAPVKTPURC4BEKTO/bundle.json","state_url":"https://pith.science/pith/36MJTPD42THAPVKTPURC4BEKTO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/36MJTPD42THAPVKTPURC4BEKTO/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":"2026-05-25T13:59:54Z","links":{"resolver":"https://pith.science/pith/36MJTPD42THAPVKTPURC4BEKTO","bundle":"https://pith.science/pith/36MJTPD42THAPVKTPURC4BEKTO/bundle.json","state":"https://pith.science/pith/36MJTPD42THAPVKTPURC4BEKTO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/36MJTPD42THAPVKTPURC4BEKTO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:36MJTPD42THAPVKTPURC4BEKTO","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":"f18903020e0aea2ebc5c79808ea68b67cc0f2dcc0685f7f6601672b29fdbd8be","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T08:39:49Z","title_canon_sha256":"e11c876e0ab3582420d25c298bd7697b9444f0bb621634c5f571a4bc224c44e7"},"schema_version":"1.0","source":{"id":"1903.11834","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11834","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11834v1","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11834","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"36MJTPD42THA","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"36MJTPD42THAPVKT","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"36MJTPD4","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:b8a226bc80061bf94932e5721474cd33f65d866d8d8a7153c7c8641e05dd6715","target":"graph","created_at":"2026-05-17T23:49:58Z","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"},"paper":{"abstract_excerpt":"Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion","authors_text":"Pingkun Yan, Rong Zhang, Xueying Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T08:39:49Z","title":"Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11834","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:fb9f73bd67424be8e7817595d4b3444d10284b592e5e6b3d0d2995e6488edadd","target":"record","created_at":"2026-05-17T23:49:58Z","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":"f18903020e0aea2ebc5c79808ea68b67cc0f2dcc0685f7f6601672b29fdbd8be","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T08:39:49Z","title_canon_sha256":"e11c876e0ab3582420d25c298bd7697b9444f0bb621634c5f571a4bc224c44e7"},"schema_version":"1.0","source":{"id":"1903.11834","kind":"arxiv","version":1}},"canonical_sha256":"df9899bc7cd4ce07d5537d222e048a9b8bf664513e29cacab69ff30a171e6c9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df9899bc7cd4ce07d5537d222e048a9b8bf664513e29cacab69ff30a171e6c9b","first_computed_at":"2026-05-17T23:49:58.969409Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:58.969409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3e4qteafY3m0AiopJQmjtlEminfYJY2nv9rwmBkjcjbSOYqj3r5nXUUEaGl2A5pVzH8mkG8Wt7557qBA7+i+DQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:58.969845Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.11834","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb9f73bd67424be8e7817595d4b3444d10284b592e5e6b3d0d2995e6488edadd","sha256:b8a226bc80061bf94932e5721474cd33f65d866d8d8a7153c7c8641e05dd6715"],"state_sha256":"18163b05eb61338df01d25801cbe6de9a6920506a4e1b81a50d59c816e0bb28c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L+CDOcXOtCO4KQc/VDBzPZK5xJHH1I6rYUMjHZ9mrp1RD5XpdEoKt4Iypa1G+BHQyOg9qXnAkSQb+0i8np+xBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T13:59:54.151435Z","bundle_sha256":"ce776c2dd18f613413f09026c30f0029c1aa565b9ece9f6512fe2e1b05efb8f2"}}