{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:67YQAQABR62VNTIALPGBX5WVXV","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":"dbcc4c94391932fb5a27de7744310d6c22ed0f30336f4cb806f529c47ee74188","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-19T13:23:43Z","title_canon_sha256":"83846c83dbd42bf0faacc9359c3d263a87f5698a2180a69b7959d1341e5cb75a"},"schema_version":"1.0","source":{"id":"2101.07612","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.07612","created_at":"2026-07-05T02:07:59Z"},{"alias_kind":"arxiv_version","alias_value":"2101.07612v1","created_at":"2026-07-05T02:07:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07612","created_at":"2026-07-05T02:07:59Z"},{"alias_kind":"pith_short_12","alias_value":"67YQAQABR62V","created_at":"2026-07-05T02:07:59Z"},{"alias_kind":"pith_short_16","alias_value":"67YQAQABR62VNTIA","created_at":"2026-07-05T02:07:59Z"},{"alias_kind":"pith_short_8","alias_value":"67YQAQAB","created_at":"2026-07-05T02:07:59Z"}],"graph_snapshots":[{"event_id":"sha256:9f0fc61f9e8253f5b766a7be47374e97805d84f2c59d528b8751c3b16562b626","target":"graph","created_at":"2026-07-05T02:07:59Z","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/2101.07612/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image segmentation plays a pivotal role in several medical-imaging applications by assisting the segmentation of the regions of interest. Deep learning-based approaches have been widely adopted for semantic segmentation of medical data. In recent years, in addition to 2D deep learning architectures, 3D architectures have been employed as the predictive algorithms for 3D medical image data. In this paper, we propose a 3D stack-based deep learning technique for segmenting manifestations of consolidation and ground-glass opacities in 3D Computed Tomography (CT) scans. We also present a comparison","authors_text":"Abhishek Shivdeo, Amit Kharat, Aniruddha Pant, Rohit Lokwani, Viraj Kulkarni","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-19T13:23:43Z","title":"Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07612","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":"sha256:84afd1f1e3ee51f3c601b7e8e86637646db281edfb6ea428ba3895496093f420","target":"record","created_at":"2026-07-05T02:07:59Z","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":"dbcc4c94391932fb5a27de7744310d6c22ed0f30336f4cb806f529c47ee74188","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-19T13:23:43Z","title_canon_sha256":"83846c83dbd42bf0faacc9359c3d263a87f5698a2180a69b7959d1341e5cb75a"},"schema_version":"1.0","source":{"id":"2101.07612","kind":"arxiv","version":1}},"canonical_sha256":"f7f10040018fb556cd005bcc1bf6d5bd4bf603f7682937bccf95c3f94e232999","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7f10040018fb556cd005bcc1bf6d5bd4bf603f7682937bccf95c3f94e232999","first_computed_at":"2026-07-05T02:07:59.598531Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:07:59.598531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+stVH5bCGrX9MaAY1YOpjMblj4WPQJNeiWmpYVkGuRwafuWbG+qY9xB9QhyhZ/hBqYcg43IBpopLmcm8qBtPBw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:07:59.598933Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.07612","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84afd1f1e3ee51f3c601b7e8e86637646db281edfb6ea428ba3895496093f420","sha256:9f0fc61f9e8253f5b766a7be47374e97805d84f2c59d528b8751c3b16562b626"],"state_sha256":"206c8094567ce6cb5d11255532e1ac4ae532a04d6ec684be767b36a83152af76"}