{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UYXTFTWS56AZVGICIBQZNQVXIG","short_pith_number":"pith:UYXTFTWS","canonical_record":{"source":{"id":"1701.05616","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-19T21:52:21Z","cross_cats_sorted":[],"title_canon_sha256":"13c499a39f2c43635dd1ad8aec080728ea719d229ad4d6673f67ceda808259cb","abstract_canon_sha256":"f99175dc3956a9e43bb52531330a5b3443860ba9d47f08bd21057effe63bbf27"},"schema_version":"1.0"},"canonical_sha256":"a62f32ced2ef819a9902406196c2b74194a366f1c9cd36acfdb9980b79031d66","source":{"kind":"arxiv","id":"1701.05616","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.05616","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"1701.05616v1","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.05616","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"UYXTFTWS56AZ","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"UYXTFTWS56AZVGIC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"UYXTFTWS","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UYXTFTWS56AZVGICIBQZNQVXIG","target":"record","payload":{"canonical_record":{"source":{"id":"1701.05616","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-19T21:52:21Z","cross_cats_sorted":[],"title_canon_sha256":"13c499a39f2c43635dd1ad8aec080728ea719d229ad4d6673f67ceda808259cb","abstract_canon_sha256":"f99175dc3956a9e43bb52531330a5b3443860ba9d47f08bd21057effe63bbf27"},"schema_version":"1.0"},"canonical_sha256":"a62f32ced2ef819a9902406196c2b74194a366f1c9cd36acfdb9980b79031d66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:28.990330Z","signature_b64":"zsQgl/uabtd6gkSDQ2BtJyDOuKwRvOZSrgfhUEK+pAX3TT39hi2rkkO8yZ/u6rf02dCWWs+w0gbY6uZjhRqSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a62f32ced2ef819a9902406196c2b74194a366f1c9cd36acfdb9980b79031d66","last_reissued_at":"2026-05-18T00:52:28.989904Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:28.989904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.05616","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nwZnYKl9KVFqoHw/Xl8Ez9y5hsg7s5lQ1GToD+fiTviO5tP/rDdYBJcSPI1TsIBKDHpsslPok9q4WyHHpPnsDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:55:47.459784Z"},"content_sha256":"24331b6774e6de5bbf0b94660ff09d18c4aa661a3d7c2365290518aaf018d7ea","schema_version":"1.0","event_id":"sha256:24331b6774e6de5bbf0b94660ff09d18c4aa661a3d7c2365290518aaf018d7ea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UYXTFTWS56AZVGICIBQZNQVXIG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam P. Harrison, Daniel J. Mollura, Le Lu, Mingchen Gao, Ronald M. Summers, Ziyue Xu","submitted_at":"2017-01-19T21:52:21Z","abstract_excerpt":"Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal. The majority of existing work relies on manually-provided ILD ROIs to extract sampled 2D image patches from CT slices and, from there, performs patch-based ILD categorization. Acquiring manual ROIs is labor intensive and serves as a bottleneck towards fully-automated CT imaging ILD screening over large-scale populations. Furthermore,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.05616","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":""},"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":"2026-05-18T00:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oo+1k7m3sOoBSv21wXUri9uiPHjpyfhSxPENcYLVGzxPleUHb5UQQYec9AJBlFLVhfXoL1qYJSo+mPZsyY+DAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:55:47.460181Z"},"content_sha256":"5dc739a81594334e4379f68416f51189ca9c0c4dfeb09dc9bff483aab735d9dd","schema_version":"1.0","event_id":"sha256:5dc739a81594334e4379f68416f51189ca9c0c4dfeb09dc9bff483aab735d9dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UYXTFTWS56AZVGICIBQZNQVXIG/bundle.json","state_url":"https://pith.science/pith/UYXTFTWS56AZVGICIBQZNQVXIG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UYXTFTWS56AZVGICIBQZNQVXIG/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":"2026-05-26T13:55:47Z","links":{"resolver":"https://pith.science/pith/UYXTFTWS56AZVGICIBQZNQVXIG","bundle":"https://pith.science/pith/UYXTFTWS56AZVGICIBQZNQVXIG/bundle.json","state":"https://pith.science/pith/UYXTFTWS56AZVGICIBQZNQVXIG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UYXTFTWS56AZVGICIBQZNQVXIG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UYXTFTWS56AZVGICIBQZNQVXIG","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":"f99175dc3956a9e43bb52531330a5b3443860ba9d47f08bd21057effe63bbf27","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-19T21:52:21Z","title_canon_sha256":"13c499a39f2c43635dd1ad8aec080728ea719d229ad4d6673f67ceda808259cb"},"schema_version":"1.0","source":{"id":"1701.05616","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.05616","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"1701.05616v1","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.05616","created_at":"2026-05-18T00:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"UYXTFTWS56AZ","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"UYXTFTWS56AZVGIC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"UYXTFTWS","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:5dc739a81594334e4379f68416f51189ca9c0c4dfeb09dc9bff483aab735d9dd","target":"graph","created_at":"2026-05-18T00:52:28Z","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":"Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal. The majority of existing work relies on manually-provided ILD ROIs to extract sampled 2D image patches from CT slices and, from there, performs patch-based ILD categorization. Acquiring manual ROIs is labor intensive and serves as a bottleneck towards fully-automated CT imaging ILD screening over large-scale populations. Furthermore,","authors_text":"Adam P. Harrison, Daniel J. Mollura, Le Lu, Mingchen Gao, Ronald M. Summers, Ziyue Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-19T21:52:21Z","title":"Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.05616","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":"sha256:24331b6774e6de5bbf0b94660ff09d18c4aa661a3d7c2365290518aaf018d7ea","target":"record","created_at":"2026-05-18T00:52:28Z","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":"f99175dc3956a9e43bb52531330a5b3443860ba9d47f08bd21057effe63bbf27","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-19T21:52:21Z","title_canon_sha256":"13c499a39f2c43635dd1ad8aec080728ea719d229ad4d6673f67ceda808259cb"},"schema_version":"1.0","source":{"id":"1701.05616","kind":"arxiv","version":1}},"canonical_sha256":"a62f32ced2ef819a9902406196c2b74194a366f1c9cd36acfdb9980b79031d66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a62f32ced2ef819a9902406196c2b74194a366f1c9cd36acfdb9980b79031d66","first_computed_at":"2026-05-18T00:52:28.989904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:28.989904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zsQgl/uabtd6gkSDQ2BtJyDOuKwRvOZSrgfhUEK+pAX3TT39hi2rkkO8yZ/u6rf02dCWWs+w0gbY6uZjhRqSCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:28.990330Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.05616","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:24331b6774e6de5bbf0b94660ff09d18c4aa661a3d7c2365290518aaf018d7ea","sha256:5dc739a81594334e4379f68416f51189ca9c0c4dfeb09dc9bff483aab735d9dd"],"state_sha256":"0f3aa248c87ae5328ffdda528e02600cb71c847a06bc699cb0d10914f3d99616"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lzggszrlLMA0bYNeMRGtU0lJ4IqN2Uyies5XxZKrwVVfTlKNMVonhyJW5KjJrV975wb1g0ZnzAAS+E6K/D0oBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T13:55:47.463225Z","bundle_sha256":"1addecbe53dc014d779dcab0010bcd64cf66f95329878aa84ea6ec97d380dd6d"}}