{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TE3Q565JREPTACV7BCFHQ4FIHZ","short_pith_number":"pith:TE3Q565J","canonical_record":{"source":{"id":"1710.08149","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.CB","submitted_at":"2017-10-23T08:53:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2da8a8bdf51b977851aa49c28c6d201a4a6b53711956c986bc1b07673541a9b9","abstract_canon_sha256":"f03ebb86aef036c57d7361015d4551b13913823483e025c0e48dc372bb0bda8c"},"schema_version":"1.0"},"canonical_sha256":"99370efba9891f300abf088a7870a83e4d3171228c36d4f1ec4595047832f262","source":{"kind":"arxiv","id":"1710.08149","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08149","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08149v3","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08149","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"pith_short_12","alias_value":"TE3Q565JREPT","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TE3Q565JREPTACV7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TE3Q565J","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TE3Q565JREPTACV7BCFHQ4FIHZ","target":"record","payload":{"canonical_record":{"source":{"id":"1710.08149","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.CB","submitted_at":"2017-10-23T08:53:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2da8a8bdf51b977851aa49c28c6d201a4a6b53711956c986bc1b07673541a9b9","abstract_canon_sha256":"f03ebb86aef036c57d7361015d4551b13913823483e025c0e48dc372bb0bda8c"},"schema_version":"1.0"},"canonical_sha256":"99370efba9891f300abf088a7870a83e4d3171228c36d4f1ec4595047832f262","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:50.167764Z","signature_b64":"UkXpYOCPPQLZ5qLqsWPp6VwV0M3DvUDT36JjtLHBDMW6mHiJkdEGmyw80sFsRI22kvDYM+HeUqLRXd+HYko6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99370efba9891f300abf088a7870a83e4d3171228c36d4f1ec4595047832f262","last_reissued_at":"2026-05-18T00:31:50.167058Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:50.167058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.08149","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cTaQHr6KYqYarXVMIbCsSbYz6IhOSDbr5XhYlv5uhLwtyh9AfcIQZA5PfJKjwk5/HXiBXBVBZ6UFWIHdzZQxAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:20:25.205235Z"},"content_sha256":"56325e181582e2fe69fc13c1841eb2eb9526feb72545f81807bf6143ca9aafb2","schema_version":"1.0","event_id":"sha256:56325e181582e2fe69fc13c1841eb2eb9526feb72545f81807bf6143ca9aafb2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TE3Q565JREPTACV7BCFHQ4FIHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"q-bio.CB","authors_text":"Mengjia Xu, Mo Zhang, Quanzheng Li, Xiang Li","submitted_at":"2017-10-23T08:53:07Z","abstract_excerpt":"Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the deep U-Net structure with deformable convolution layers. The U-Net architecture for deep learning has been shown "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08149","kind":"arxiv","version":3},"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":"2026-05-18T00:31:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"62KXPPgwFlqR9zsy+ik1KOpsvTtk6HP0unljztPCMFMnCuHTPI3phgIjprIp1xZZ7xQ2L4YauiCZuLWV4AWsBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:20:25.205601Z"},"content_sha256":"1a7e893ba4202209cfb2b282347afd49db725a989492681d4031d347c51008ec","schema_version":"1.0","event_id":"sha256:1a7e893ba4202209cfb2b282347afd49db725a989492681d4031d347c51008ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/bundle.json","state_url":"https://pith.science/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/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":"2026-05-28T02:20:25Z","links":{"resolver":"https://pith.science/pith/TE3Q565JREPTACV7BCFHQ4FIHZ","bundle":"https://pith.science/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/bundle.json","state":"https://pith.science/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TE3Q565JREPTACV7BCFHQ4FIHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TE3Q565JREPTACV7BCFHQ4FIHZ","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":"f03ebb86aef036c57d7361015d4551b13913823483e025c0e48dc372bb0bda8c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.CB","submitted_at":"2017-10-23T08:53:07Z","title_canon_sha256":"2da8a8bdf51b977851aa49c28c6d201a4a6b53711956c986bc1b07673541a9b9"},"schema_version":"1.0","source":{"id":"1710.08149","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08149","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08149v3","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08149","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"pith_short_12","alias_value":"TE3Q565JREPT","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TE3Q565JREPTACV7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TE3Q565J","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:1a7e893ba4202209cfb2b282347afd49db725a989492681d4031d347c51008ec","target":"graph","created_at":"2026-05-18T00:31:50Z","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":"Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the deep U-Net structure with deformable convolution layers. The U-Net architecture for deep learning has been shown ","authors_text":"Mengjia Xu, Mo Zhang, Quanzheng Li, Xiang Li","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.CB","submitted_at":"2017-10-23T08:53:07Z","title":"Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08149","kind":"arxiv","version":3},"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":"sha256:56325e181582e2fe69fc13c1841eb2eb9526feb72545f81807bf6143ca9aafb2","target":"record","created_at":"2026-05-18T00:31:50Z","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":"f03ebb86aef036c57d7361015d4551b13913823483e025c0e48dc372bb0bda8c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.CB","submitted_at":"2017-10-23T08:53:07Z","title_canon_sha256":"2da8a8bdf51b977851aa49c28c6d201a4a6b53711956c986bc1b07673541a9b9"},"schema_version":"1.0","source":{"id":"1710.08149","kind":"arxiv","version":3}},"canonical_sha256":"99370efba9891f300abf088a7870a83e4d3171228c36d4f1ec4595047832f262","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99370efba9891f300abf088a7870a83e4d3171228c36d4f1ec4595047832f262","first_computed_at":"2026-05-18T00:31:50.167058Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:50.167058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UkXpYOCPPQLZ5qLqsWPp6VwV0M3DvUDT36JjtLHBDMW6mHiJkdEGmyw80sFsRI22kvDYM+HeUqLRXd+HYko6Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:50.167764Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.08149","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56325e181582e2fe69fc13c1841eb2eb9526feb72545f81807bf6143ca9aafb2","sha256:1a7e893ba4202209cfb2b282347afd49db725a989492681d4031d347c51008ec"],"state_sha256":"539cd8cc8841043d13c720c136ac99b0829f60cbaffce5626f01db7aec187473"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SwAwgmc/KWO0b6bpo6JLapUb53+nPHrSUOrXyLPetVCwE1pKDTUp5plY60xKnA6wbXHP8BRoAQ2el8b13vD9Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T02:20:25.207756Z","bundle_sha256":"7c679cfb9ce29ee2e48b0402900796fea7b2fc346ee78f36aefc86b19765babf"}}