{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:U5E7JJQDCTN33CJCLSOMYKXHC3","short_pith_number":"pith:U5E7JJQD","canonical_record":{"source":{"id":"2204.11294","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-04-24T14:55:57Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"393d6fb1b2e13c9be2a39e1a018d06022da7c63fb506888b2b66045a1442b3a4","abstract_canon_sha256":"500c7518aacd10e308cafe3aa7da8e1464de12fd8b402c4b8d50e87ca6903ca4"},"schema_version":"1.0"},"canonical_sha256":"a749f4a60314dbbd89225c9ccc2ae716d57ef348a14af7bf8241a5b0e2ce36e7","source":{"kind":"arxiv","id":"2204.11294","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.11294","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"arxiv_version","alias_value":"2204.11294v2","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11294","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_12","alias_value":"U5E7JJQDCTN3","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_16","alias_value":"U5E7JJQDCTN33CJC","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_8","alias_value":"U5E7JJQD","created_at":"2026-07-05T05:20:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:U5E7JJQDCTN33CJCLSOMYKXHC3","target":"record","payload":{"canonical_record":{"source":{"id":"2204.11294","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-04-24T14:55:57Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"393d6fb1b2e13c9be2a39e1a018d06022da7c63fb506888b2b66045a1442b3a4","abstract_canon_sha256":"500c7518aacd10e308cafe3aa7da8e1464de12fd8b402c4b8d50e87ca6903ca4"},"schema_version":"1.0"},"canonical_sha256":"a749f4a60314dbbd89225c9ccc2ae716d57ef348a14af7bf8241a5b0e2ce36e7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:36.986854Z","signature_b64":"byNcKxNAYEmdui4CXg2VW+/z74GEjB1U+/hAJsBpZLVPkKhwZ1k7gBNG+na7sK+rDoeKqIGtLJhyS5eP8ri4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a749f4a60314dbbd89225c9ccc2ae716d57ef348a14af7bf8241a5b0e2ce36e7","last_reissued_at":"2026-07-05T05:20:36.986365Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:36.986365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.11294","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":"2026-07-05T05:20:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hg016XKp8sNfYd/GQoZrWXWTh40z8jbRUI9n35uQVVEYgg5gBh5FdAvE2oVlSHwcEPBnj+c1c1a5+tog8shdAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T01:14:19.369087Z"},"content_sha256":"cc7f5eca5d2105106280cfa939af27bf087cd7964097830733a4a9cadc534c4a","schema_version":"1.0","event_id":"sha256:cc7f5eca5d2105106280cfa939af27bf087cd7964097830733a4a9cadc534c4a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:U5E7JJQDCTN33CJCLSOMYKXHC3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Colorectal cancer survival prediction using deep distribution based multiple-instance learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hong Zhang, Jitendra Jonnagaddala, Min Cen, Xingyu Li, Xu Steven Xu","submitted_at":"2022-04-24T14:55:57Z","abstract_excerpt":"Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms. Most deep learning based Multiple Instance Learning (MIL) algorithms for survival prediction use either top instances (e.g., maxpooling) or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this study, we hypothesize that wholistic information of the distribution of the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11294","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/2204.11294/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":"2026-07-05T05:20:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJCO+KMl1BpFDzjBotDvu2w1H8aspgkV+R/LQrip0IuZpzJTuvlWPdJoOTMNFNPASCw7DN4JhKQVzr4wV6j0CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T01:14:19.369484Z"},"content_sha256":"358f4a5192aeb05052645cc208335daa85b9ad3ead679862385047135c110cad","schema_version":"1.0","event_id":"sha256:358f4a5192aeb05052645cc208335daa85b9ad3ead679862385047135c110cad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/bundle.json","state_url":"https://pith.science/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/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":"2026-07-28T01:14:19Z","links":{"resolver":"https://pith.science/pith/U5E7JJQDCTN33CJCLSOMYKXHC3","bundle":"https://pith.science/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/bundle.json","state":"https://pith.science/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U5E7JJQDCTN33CJCLSOMYKXHC3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:U5E7JJQDCTN33CJCLSOMYKXHC3","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":"500c7518aacd10e308cafe3aa7da8e1464de12fd8b402c4b8d50e87ca6903ca4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-04-24T14:55:57Z","title_canon_sha256":"393d6fb1b2e13c9be2a39e1a018d06022da7c63fb506888b2b66045a1442b3a4"},"schema_version":"1.0","source":{"id":"2204.11294","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.11294","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"arxiv_version","alias_value":"2204.11294v2","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11294","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_12","alias_value":"U5E7JJQDCTN3","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_16","alias_value":"U5E7JJQDCTN33CJC","created_at":"2026-07-05T05:20:36Z"},{"alias_kind":"pith_short_8","alias_value":"U5E7JJQD","created_at":"2026-07-05T05:20:36Z"}],"graph_snapshots":[{"event_id":"sha256:358f4a5192aeb05052645cc208335daa85b9ad3ead679862385047135c110cad","target":"graph","created_at":"2026-07-05T05:20:36Z","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/2204.11294/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms. Most deep learning based Multiple Instance Learning (MIL) algorithms for survival prediction use either top instances (e.g., maxpooling) or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this study, we hypothesize that wholistic information of the distribution of the","authors_text":"Hong Zhang, Jitendra Jonnagaddala, Min Cen, Xingyu Li, Xu Steven Xu","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-04-24T14:55:57Z","title":"Colorectal cancer survival prediction using deep distribution based multiple-instance learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11294","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:cc7f5eca5d2105106280cfa939af27bf087cd7964097830733a4a9cadc534c4a","target":"record","created_at":"2026-07-05T05:20:36Z","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":"500c7518aacd10e308cafe3aa7da8e1464de12fd8b402c4b8d50e87ca6903ca4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-04-24T14:55:57Z","title_canon_sha256":"393d6fb1b2e13c9be2a39e1a018d06022da7c63fb506888b2b66045a1442b3a4"},"schema_version":"1.0","source":{"id":"2204.11294","kind":"arxiv","version":2}},"canonical_sha256":"a749f4a60314dbbd89225c9ccc2ae716d57ef348a14af7bf8241a5b0e2ce36e7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a749f4a60314dbbd89225c9ccc2ae716d57ef348a14af7bf8241a5b0e2ce36e7","first_computed_at":"2026-07-05T05:20:36.986365Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:20:36.986365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"byNcKxNAYEmdui4CXg2VW+/z74GEjB1U+/hAJsBpZLVPkKhwZ1k7gBNG+na7sK+rDoeKqIGtLJhyS5eP8ri4AA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:20:36.986854Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.11294","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc7f5eca5d2105106280cfa939af27bf087cd7964097830733a4a9cadc534c4a","sha256:358f4a5192aeb05052645cc208335daa85b9ad3ead679862385047135c110cad"],"state_sha256":"6407575ada3c1a9cbbbbdac2587de27d16b24421d49171bfd2156a24676f65f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q9Jx4zoT8zPXwuU1U3Lhh/QN30COPJDeHRP+G6HYIi8oetQnLN369SHXUP/SK+PqePqKR2TpvbzO3RkfHjYOBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-28T01:14:19.371927Z","bundle_sha256":"375b05577fe2fd17803e5e8dcbab5ef4fe7a99751e3c241c02a06ea855d5b474"}}