{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PG3HECIC4LPH7DAX3AEBQTPN3K","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":"d60ed46a3b5741b6f96bba42f678c993b88c6fb8fa713bf52ff41ffd1b483e61","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-08-23T05:18:19Z","title_canon_sha256":"c94dea20f3396a85d057b77b73dc2b2334d14de8197668f86f44f290b27968f2"},"schema_version":"1.0","source":{"id":"2208.10737","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.10737","created_at":"2026-07-05T04:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"2208.10737v1","created_at":"2026-07-05T04:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.10737","created_at":"2026-07-05T04:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"PG3HECIC4LPH","created_at":"2026-07-05T04:50:39Z"},{"alias_kind":"pith_short_16","alias_value":"PG3HECIC4LPH7DAX","created_at":"2026-07-05T04:50:39Z"},{"alias_kind":"pith_short_8","alias_value":"PG3HECIC","created_at":"2026-07-05T04:50:39Z"}],"graph_snapshots":[{"event_id":"sha256:6421afdf4ad169978a00e8ed86915ee615b80d27bd1017c257e6bfacc50a5ca3","target":"graph","created_at":"2026-07-05T04:50:39Z","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/2208.10737/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, a semi-automatic annotation of bacteria genera and species from DIBaS dataset is implemented using clustering and thresholding algorithms. A Deep learning model is trained to achieve the semantic segmentation and classification of the bacteria species. Classification accuracy of 95% is achieved. Deep learning models find tremendous applications in biomedical image processing. Automatic segmentation of bacteria from gram-stained microscopic images is essential to diagnose respiratory and urinary tract infections, detect cancers, etc. Deep learning will aid the biologists to get r","authors_text":"Chethan Reddy G.P., Deepu Vijayasenan, Pullagurla Abhijith Reddy, Sreejith Govindan, Sumam S. David, Vidyashree R. Kanabur","cross_cats":["cs.CV","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-08-23T05:18:19Z","title":"Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.10737","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":"sha256:42daee308b9ea78aa42897e141873325f6fe396f181a268e558892af99ffc9c4","target":"record","created_at":"2026-07-05T04:50:39Z","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":"d60ed46a3b5741b6f96bba42f678c993b88c6fb8fa713bf52ff41ffd1b483e61","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-08-23T05:18:19Z","title_canon_sha256":"c94dea20f3396a85d057b77b73dc2b2334d14de8197668f86f44f290b27968f2"},"schema_version":"1.0","source":{"id":"2208.10737","kind":"arxiv","version":1}},"canonical_sha256":"79b6720902e2de7f8c17d808184dedda9ca3ce0324502d8a72b2b208f7c64812","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79b6720902e2de7f8c17d808184dedda9ca3ce0324502d8a72b2b208f7c64812","first_computed_at":"2026-07-05T04:50:39.523051Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:50:39.523051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F0Qf69C7e3/d9qIybM6IRNqq53uOlMKd/qIzfB5GABH+V8l/I7EMVj6BmlgunMiZcimNunh7c8ne4vbsUnlJCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:50:39.523492Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.10737","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:42daee308b9ea78aa42897e141873325f6fe396f181a268e558892af99ffc9c4","sha256:6421afdf4ad169978a00e8ed86915ee615b80d27bd1017c257e6bfacc50a5ca3"],"state_sha256":"b017f3fcd9ae90e7b654cbcaa2d2951914d208a661b602e0cb1689041be56acb"}