{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:O5SDLREEEHTMCIF4I36JTWH2VV","short_pith_number":"pith:O5SDLREE","canonical_record":{"source":{"id":"2301.02608","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-06T17:10:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"31acae122e6762aea6fdc7abcbee30bb0007dfbc105f3881b76fe8d6cad24751","abstract_canon_sha256":"ba8ac7944c44bad7055544c2dd379ae4cfb427292b4a26be928d49f4b942fc09"},"schema_version":"1.0"},"canonical_sha256":"776435c48421e6c120bc46fc99d8faad48b8e78e1e39a844384280b02e3196c4","source":{"kind":"arxiv","id":"2301.02608","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.02608","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"arxiv_version","alias_value":"2301.02608v2","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.02608","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_12","alias_value":"O5SDLREEEHTM","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_16","alias_value":"O5SDLREEEHTMCIF4","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_8","alias_value":"O5SDLREE","created_at":"2026-07-05T08:13:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:O5SDLREEEHTMCIF4I36JTWH2VV","target":"record","payload":{"canonical_record":{"source":{"id":"2301.02608","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-06T17:10:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"31acae122e6762aea6fdc7abcbee30bb0007dfbc105f3881b76fe8d6cad24751","abstract_canon_sha256":"ba8ac7944c44bad7055544c2dd379ae4cfb427292b4a26be928d49f4b942fc09"},"schema_version":"1.0"},"canonical_sha256":"776435c48421e6c120bc46fc99d8faad48b8e78e1e39a844384280b02e3196c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:58.053898Z","signature_b64":"OJf8gjdba7zdvHmM4wVo7Sj+pkayNMDxOFNhiELmy/w2SHBAqrbdYIkU0YJFCVckRTDsyJiCUNNH6mVeDwz/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"776435c48421e6c120bc46fc99d8faad48b8e78e1e39a844384280b02e3196c4","last_reissued_at":"2026-07-05T08:13:58.053331Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:58.053331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.02608","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:13:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xOso233aWnNNzltto+m6VosHTLdRSgo3NJ/+F4+a4rr70jSU2O6vobYcmwKR+wDNDDh9mcMLdBGq7R1Hc/dQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T14:43:25.926692Z"},"content_sha256":"b77dc07bba394378242cb8c7f0ea49e8668cb0f058fd6fbfd56ba57aafeee2da","schema_version":"1.0","event_id":"sha256:b77dc07bba394378242cb8c7f0ea49e8668cb0f058fd6fbfd56ba57aafeee2da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:O5SDLREEEHTMCIF4I36JTWH2VV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An interpretable machine learning system for colorectal cancer diagnosis from pathology slides","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ana Monteiro, Diana Montezuma, Domingos Oliveira, Inti Zlobec, Isabel M. Pinto, Jaime S. Cardoso, Jo\\~ao Fraga, Jo\\~ao Monteiro, Liliana Ribeiro, Pedro C. Neto, Sara P. Oliveira, Sofia Gon\\c{c}alves, Stefan Reinhard","submitted_at":"2023-01-06T17:10:32Z","abstract_excerpt":"Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.02608","kind":"arxiv","version":2},"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/2301.02608/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:13:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xte+RM1CU8VYP0W3vYEPyovQmadWKlBtL9xFmvLWN2PpXKwky0KNBKW2FfP+xE0DvPBS9LaF28NGFJUirGQHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T14:43:25.927085Z"},"content_sha256":"136b0231556a1b9d9d7c194cc15b24754d2e903f082ac98d2929163f23a942bc","schema_version":"1.0","event_id":"sha256:136b0231556a1b9d9d7c194cc15b24754d2e903f082ac98d2929163f23a942bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5SDLREEEHTMCIF4I36JTWH2VV/bundle.json","state_url":"https://pith.science/pith/O5SDLREEEHTMCIF4I36JTWH2VV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5SDLREEEHTMCIF4I36JTWH2VV/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":"2026-07-11T14:43:25Z","links":{"resolver":"https://pith.science/pith/O5SDLREEEHTMCIF4I36JTWH2VV","bundle":"https://pith.science/pith/O5SDLREEEHTMCIF4I36JTWH2VV/bundle.json","state":"https://pith.science/pith/O5SDLREEEHTMCIF4I36JTWH2VV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5SDLREEEHTMCIF4I36JTWH2VV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:O5SDLREEEHTMCIF4I36JTWH2VV","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":"ba8ac7944c44bad7055544c2dd379ae4cfb427292b4a26be928d49f4b942fc09","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-06T17:10:32Z","title_canon_sha256":"31acae122e6762aea6fdc7abcbee30bb0007dfbc105f3881b76fe8d6cad24751"},"schema_version":"1.0","source":{"id":"2301.02608","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.02608","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"arxiv_version","alias_value":"2301.02608v2","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.02608","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_12","alias_value":"O5SDLREEEHTM","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_16","alias_value":"O5SDLREEEHTMCIF4","created_at":"2026-07-05T08:13:58Z"},{"alias_kind":"pith_short_8","alias_value":"O5SDLREE","created_at":"2026-07-05T08:13:58Z"}],"graph_snapshots":[{"event_id":"sha256:136b0231556a1b9d9d7c194cc15b24754d2e903f082ac98d2929163f23a942bc","target":"graph","created_at":"2026-07-05T08:13:58Z","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/2301.02608/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this st","authors_text":"Ana Monteiro, Diana Montezuma, Domingos Oliveira, Inti Zlobec, Isabel M. Pinto, Jaime S. Cardoso, Jo\\~ao Fraga, Jo\\~ao Monteiro, Liliana Ribeiro, Pedro C. Neto, Sara P. Oliveira, Sofia Gon\\c{c}alves, Stefan Reinhard","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-06T17:10:32Z","title":"An interpretable machine learning system for colorectal cancer diagnosis from pathology slides"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.02608","kind":"arxiv","version":2},"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:b77dc07bba394378242cb8c7f0ea49e8668cb0f058fd6fbfd56ba57aafeee2da","target":"record","created_at":"2026-07-05T08:13:58Z","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":"ba8ac7944c44bad7055544c2dd379ae4cfb427292b4a26be928d49f4b942fc09","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-06T17:10:32Z","title_canon_sha256":"31acae122e6762aea6fdc7abcbee30bb0007dfbc105f3881b76fe8d6cad24751"},"schema_version":"1.0","source":{"id":"2301.02608","kind":"arxiv","version":2}},"canonical_sha256":"776435c48421e6c120bc46fc99d8faad48b8e78e1e39a844384280b02e3196c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"776435c48421e6c120bc46fc99d8faad48b8e78e1e39a844384280b02e3196c4","first_computed_at":"2026-07-05T08:13:58.053331Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:13:58.053331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OJf8gjdba7zdvHmM4wVo7Sj+pkayNMDxOFNhiELmy/w2SHBAqrbdYIkU0YJFCVckRTDsyJiCUNNH6mVeDwz/BA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:13:58.053898Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.02608","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b77dc07bba394378242cb8c7f0ea49e8668cb0f058fd6fbfd56ba57aafeee2da","sha256:136b0231556a1b9d9d7c194cc15b24754d2e903f082ac98d2929163f23a942bc"],"state_sha256":"70a1aab4c572df03b4c2a8534f3a6a5352c3d10f1213807fa268bae06dec5614"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RLM49grsrUiIWEO+molMGf6MTIf2WiP1Mm/QT0ri5huzddYjP7eguv1gymZL/Gsrq1Za22EnjMnb6t0ujLPBDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-11T14:43:25.929426Z","bundle_sha256":"de0afb2eaf140de6f4a04279ffb454161bce3dd67b86547a24a9afab95c7c7ae"}}