{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UPC53SFKIL6JTVN6K2454JQQCL","short_pith_number":"pith:UPC53SFK","schema_version":"1.0","canonical_sha256":"a3c5ddc8aa42fc99d5be56b9de261012e289c19901676c643e00e63bbf1aa216","source":{"kind":"arxiv","id":"2605.24128","version":1},"attestation_state":"computed","paper":{"title":"ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gunjan Shrivastava, Saad Nadeem","submitted_at":"2026-05-22T18:42:46Z","abstract_excerpt":"Accurate cell segmentation in pathology images typically requires dense pixel-wise annotations, which are costly and time-consuming to obtain. This challenge is especially important for emerging biological imaging modalities and multiplexed datasets with variable channel configurations, where expert-labeled data are scarce. In this work, we introduce ImPartial, a deep learning framework designed to achieve state-of-the-art segmentation performance in low-annotation regimes using sparse scribbles and limited supervision. ImPartial augments the segmentation objective via self-supervised multi-ch"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.24128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T18:42:46Z","cross_cats_sorted":[],"title_canon_sha256":"b9c387fd017b03bba5cf840b70720de30f78c8e61b71a68382af050acba43680","abstract_canon_sha256":"846a7be0c86281ee64fa7461052bcf59256a98e254d082b15114eb8931b755db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:47.785196Z","signature_b64":"Wsijbo/0iZGUTFWFcUVt2nagxL9rpJeLxj1JQsge21K/LtUwsx7M59T3YwWC9gMhpwcTIS791N80Ixq85ULGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3c5ddc8aa42fc99d5be56b9de261012e289c19901676c643e00e63bbf1aa216","last_reissued_at":"2026-05-26T01:02:47.784378Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:47.784378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gunjan Shrivastava, Saad Nadeem","submitted_at":"2026-05-22T18:42:46Z","abstract_excerpt":"Accurate cell segmentation in pathology images typically requires dense pixel-wise annotations, which are costly and time-consuming to obtain. This challenge is especially important for emerging biological imaging modalities and multiplexed datasets with variable channel configurations, where expert-labeled data are scarce. In this work, we introduce ImPartial, a deep learning framework designed to achieve state-of-the-art segmentation performance in low-annotation regimes using sparse scribbles and limited supervision. ImPartial augments the segmentation objective via self-supervised multi-ch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24128","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/2605.24128/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.24128","created_at":"2026-05-26T01:02:47.784522+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24128v1","created_at":"2026-05-26T01:02:47.784522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24128","created_at":"2026-05-26T01:02:47.784522+00:00"},{"alias_kind":"pith_short_12","alias_value":"UPC53SFKIL6J","created_at":"2026-05-26T01:02:47.784522+00:00"},{"alias_kind":"pith_short_16","alias_value":"UPC53SFKIL6JTVN6","created_at":"2026-05-26T01:02:47.784522+00:00"},{"alias_kind":"pith_short_8","alias_value":"UPC53SFK","created_at":"2026-05-26T01:02:47.784522+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL","json":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL.json","graph_json":"https://pith.science/api/pith-number/UPC53SFKIL6JTVN6K2454JQQCL/graph.json","events_json":"https://pith.science/api/pith-number/UPC53SFKIL6JTVN6K2454JQQCL/events.json","paper":"https://pith.science/paper/UPC53SFK"},"agent_actions":{"view_html":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL","download_json":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL.json","view_paper":"https://pith.science/paper/UPC53SFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24128&json=true","fetch_graph":"https://pith.science/api/pith-number/UPC53SFKIL6JTVN6K2454JQQCL/graph.json","fetch_events":"https://pith.science/api/pith-number/UPC53SFKIL6JTVN6K2454JQQCL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL/action/storage_attestation","attest_author":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL/action/author_attestation","sign_citation":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL/action/citation_signature","submit_replication":"https://pith.science/pith/UPC53SFKIL6JTVN6K2454JQQCL/action/replication_record"}},"created_at":"2026-05-26T01:02:47.784522+00:00","updated_at":"2026-05-26T01:02:47.784522+00:00"}