{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ADI2TK7XJEFUL3TJRTLZLLHF3S","short_pith_number":"pith:ADI2TK7X","canonical_record":{"source":{"id":"1710.05726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T00:02:53Z","cross_cats_sorted":[],"title_canon_sha256":"096174dc48f78a679bcbc45b9790c0b8cc94e60370d8de8c65c0865f22c7b896","abstract_canon_sha256":"c6fb500c94bd6df8946d1600d97ad9660f20d25705b082e7c2e614e9f16849f9"},"schema_version":"1.0"},"canonical_sha256":"00d1a9abf7490b45ee698cd795ace5dcbc4d355d5433c9cf016225185ea98c3e","source":{"kind":"arxiv","id":"1710.05726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.05726","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"arxiv_version","alias_value":"1710.05726v1","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.05726","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"pith_short_12","alias_value":"ADI2TK7XJEFU","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"ADI2TK7XJEFUL3TJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"ADI2TK7X","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ADI2TK7XJEFUL3TJRTLZLLHF3S","target":"record","payload":{"canonical_record":{"source":{"id":"1710.05726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T00:02:53Z","cross_cats_sorted":[],"title_canon_sha256":"096174dc48f78a679bcbc45b9790c0b8cc94e60370d8de8c65c0865f22c7b896","abstract_canon_sha256":"c6fb500c94bd6df8946d1600d97ad9660f20d25705b082e7c2e614e9f16849f9"},"schema_version":"1.0"},"canonical_sha256":"00d1a9abf7490b45ee698cd795ace5dcbc4d355d5433c9cf016225185ea98c3e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:48.387707Z","signature_b64":"WHxkVF7ncHqjlDORmzQrPM+qWrEZbfyH2N/7Ca/Qq3UDEPeasONM0xHPPnpM0UlpVjGgpevWRwUcXF6ayCE9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00d1a9abf7490b45ee698cd795ace5dcbc4d355d5433c9cf016225185ea98c3e","last_reissued_at":"2026-05-18T00:32:48.386982Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:48.386982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.05726","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":"2026-05-18T00:32:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9vLoGn9FS2sONFPcziDDUc5tbNuHCmDo3IptJzLV6KBg32eVZ6/Ay2gaDVcth3r8dLfdTCjQqq3FVNOri9ofCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:47:13.915457Z"},"content_sha256":"4fcefccefebb63d0352ec6d190716fec41ec3dd169f52a55260ac8f0695d75cb","schema_version":"1.0","event_id":"sha256:4fcefccefebb63d0352ec6d190716fec41ec3dd169f52a55260ac8f0695d75cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ADI2TK7XJEFUL3TJRTLZLLHF3S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Brady Kieffer, H.R.Tizhoosh, Morteza Babaie, Shivam Kalra","submitted_at":"2017-10-11T00:02:53Z","abstract_excerpt":"We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.05726","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":"2026-05-18T00:32:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pLGt41kWzCCcp6LFw8MrPu+19XITOStr0ko/EhizYZCfa+Hs6igJU3NFaxE9RRU2qS9Pyoa34vjp6bwUBsCUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:47:13.915815Z"},"content_sha256":"363707397a1d41e5ae054ec40cc2d7869fcf64589f2617fc045a5ec827f56f2c","schema_version":"1.0","event_id":"sha256:363707397a1d41e5ae054ec40cc2d7869fcf64589f2617fc045a5ec827f56f2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/bundle.json","state_url":"https://pith.science/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/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":"2026-06-12T08:47:13Z","links":{"resolver":"https://pith.science/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S","bundle":"https://pith.science/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/bundle.json","state":"https://pith.science/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ADI2TK7XJEFUL3TJRTLZLLHF3S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ADI2TK7XJEFUL3TJRTLZLLHF3S","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":"c6fb500c94bd6df8946d1600d97ad9660f20d25705b082e7c2e614e9f16849f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T00:02:53Z","title_canon_sha256":"096174dc48f78a679bcbc45b9790c0b8cc94e60370d8de8c65c0865f22c7b896"},"schema_version":"1.0","source":{"id":"1710.05726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.05726","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"arxiv_version","alias_value":"1710.05726v1","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.05726","created_at":"2026-05-18T00:32:48Z"},{"alias_kind":"pith_short_12","alias_value":"ADI2TK7XJEFU","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"ADI2TK7XJEFUL3TJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"ADI2TK7X","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:363707397a1d41e5ae054ec40cc2d7869fcf64589f2617fc045a5ec827f56f2c","target":"graph","created_at":"2026-05-18T00:32:48Z","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":"We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tis","authors_text":"Brady Kieffer, H.R.Tizhoosh, Morteza Babaie, Shivam Kalra","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T00:02:53Z","title":"Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.05726","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":"sha256:4fcefccefebb63d0352ec6d190716fec41ec3dd169f52a55260ac8f0695d75cb","target":"record","created_at":"2026-05-18T00:32:48Z","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":"c6fb500c94bd6df8946d1600d97ad9660f20d25705b082e7c2e614e9f16849f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T00:02:53Z","title_canon_sha256":"096174dc48f78a679bcbc45b9790c0b8cc94e60370d8de8c65c0865f22c7b896"},"schema_version":"1.0","source":{"id":"1710.05726","kind":"arxiv","version":1}},"canonical_sha256":"00d1a9abf7490b45ee698cd795ace5dcbc4d355d5433c9cf016225185ea98c3e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00d1a9abf7490b45ee698cd795ace5dcbc4d355d5433c9cf016225185ea98c3e","first_computed_at":"2026-05-18T00:32:48.386982Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:48.386982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WHxkVF7ncHqjlDORmzQrPM+qWrEZbfyH2N/7Ca/Qq3UDEPeasONM0xHPPnpM0UlpVjGgpevWRwUcXF6ayCE9Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:48.387707Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.05726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fcefccefebb63d0352ec6d190716fec41ec3dd169f52a55260ac8f0695d75cb","sha256:363707397a1d41e5ae054ec40cc2d7869fcf64589f2617fc045a5ec827f56f2c"],"state_sha256":"f953293ec4617a2bfe052dbb169a590ff5563a16075e325a6134f95422aa6e13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Lh1dT/VlZDEguEqtoChiHbQ7D7GNtk9ZlMzfF0Pj5NgTxNzrAb3tHnTYarEjFscJe2Dsxet2ORFcr2J/9shBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T08:47:13.917676Z","bundle_sha256":"6fd0e9d0d8ed50bc21661429f6764cc3ddf0e1861dd6458506c643082512f247"}}