{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PG3HECIC4LPH7DAX3AEBQTPN3K","short_pith_number":"pith:PG3HECIC","schema_version":"1.0","canonical_sha256":"79b6720902e2de7f8c17d808184dedda9ca3ce0324502d8a72b2b208f7c64812","source":{"kind":"arxiv","id":"2208.10737","version":1},"attestation_state":"computed","paper":{"title":"Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","eess.SP"],"primary_cat":"eess.IV","authors_text":"Chethan Reddy G.P., Deepu Vijayasenan, Pullagurla Abhijith Reddy, Sreejith Govindan, Sumam S. David, Vidyashree R. Kanabur","submitted_at":"2022-08-23T05:18:19Z","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"},"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":"2208.10737","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-08-23T05:18:19Z","cross_cats_sorted":["cs.CV","eess.SP"],"title_canon_sha256":"c94dea20f3396a85d057b77b73dc2b2334d14de8197668f86f44f290b27968f2","abstract_canon_sha256":"d60ed46a3b5741b6f96bba42f678c993b88c6fb8fa713bf52ff41ffd1b483e61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:50:39.523492Z","signature_b64":"F0Qf69C7e3/d9qIybM6IRNqq53uOlMKd/qIzfB5GABH+V8l/I7EMVj6BmlgunMiZcimNunh7c8ne4vbsUnlJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79b6720902e2de7f8c17d808184dedda9ca3ce0324502d8a72b2b208f7c64812","last_reissued_at":"2026-07-05T04:50:39.523051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:50:39.523051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","eess.SP"],"primary_cat":"eess.IV","authors_text":"Chethan Reddy G.P., Deepu Vijayasenan, Pullagurla Abhijith Reddy, Sreejith Govindan, Sumam S. David, Vidyashree R. Kanabur","submitted_at":"2022-08-23T05:18:19Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.10737","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/2208.10737/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":"2208.10737","created_at":"2026-07-05T04:50:39.523109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.10737v1","created_at":"2026-07-05T04:50:39.523109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.10737","created_at":"2026-07-05T04:50:39.523109+00:00"},{"alias_kind":"pith_short_12","alias_value":"PG3HECIC4LPH","created_at":"2026-07-05T04:50:39.523109+00:00"},{"alias_kind":"pith_short_16","alias_value":"PG3HECIC4LPH7DAX","created_at":"2026-07-05T04:50:39.523109+00:00"},{"alias_kind":"pith_short_8","alias_value":"PG3HECIC","created_at":"2026-07-05T04:50:39.523109+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/PG3HECIC4LPH7DAX3AEBQTPN3K","json":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K.json","graph_json":"https://pith.science/api/pith-number/PG3HECIC4LPH7DAX3AEBQTPN3K/graph.json","events_json":"https://pith.science/api/pith-number/PG3HECIC4LPH7DAX3AEBQTPN3K/events.json","paper":"https://pith.science/paper/PG3HECIC"},"agent_actions":{"view_html":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K","download_json":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K.json","view_paper":"https://pith.science/paper/PG3HECIC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.10737&json=true","fetch_graph":"https://pith.science/api/pith-number/PG3HECIC4LPH7DAX3AEBQTPN3K/graph.json","fetch_events":"https://pith.science/api/pith-number/PG3HECIC4LPH7DAX3AEBQTPN3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K/action/storage_attestation","attest_author":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K/action/author_attestation","sign_citation":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K/action/citation_signature","submit_replication":"https://pith.science/pith/PG3HECIC4LPH7DAX3AEBQTPN3K/action/replication_record"}},"created_at":"2026-07-05T04:50:39.523109+00:00","updated_at":"2026-07-05T04:50:39.523109+00:00"}