{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZDXBA4AKKXYWTQPHBPHTL6RIKJ","short_pith_number":"pith:ZDXBA4AK","schema_version":"1.0","canonical_sha256":"c8ee10700a55f169c1e70bcf35fa285279841cde2c29767179ba928eb7c69e62","source":{"kind":"arxiv","id":"1702.05941","version":1},"attestation_state":"computed","paper":{"title":"SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Valentinitsch, Bjoern Menze, Felix Gr\\\"un, Florian Ettlinger, Freba Ahmaddy, Georgios Kaissis, Patrick Ferdinand Christ, Rickmer Braren, Sebastian Schlecht, Seyed-Ahmad Ahmadi","submitted_at":"2017-02-20T12:05:30Z","abstract_excerpt":"Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the malignancy of HCC lesions from DWI images. We predict HCC malignancy in two steps: As a first step we automatically segment HCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation. We formulate this task as a classification prob"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1702.05941","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-20T12:05:30Z","cross_cats_sorted":[],"title_canon_sha256":"e81b800a80e7219d980380a7ce2d02a2060e0453277f987061a60512d7784f40","abstract_canon_sha256":"10868e715347971673951793b06f073f2b32e80220bc64f00d8fc8059ab6f5fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:24.841878Z","signature_b64":"3U5RhSJJ5D0SfsrEC18Gr13gCxUtByiRD7yB66sRAktREvgG+rdk8h+fIYbARO190PgmEMgi28xbJyvmXRJmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8ee10700a55f169c1e70bcf35fa285279841cde2c29767179ba928eb7c69e62","last_reissued_at":"2026-05-18T00:50:24.841226Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:24.841226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Valentinitsch, Bjoern Menze, Felix Gr\\\"un, Florian Ettlinger, Freba Ahmaddy, Georgios Kaissis, Patrick Ferdinand Christ, Rickmer Braren, Sebastian Schlecht, Seyed-Ahmad Ahmadi","submitted_at":"2017-02-20T12:05:30Z","abstract_excerpt":"Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the malignancy of HCC lesions from DWI images. We predict HCC malignancy in two steps: As a first step we automatically segment HCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation. We formulate this task as a classification prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05941","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1702.05941","created_at":"2026-05-18T00:50:24.841332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.05941v1","created_at":"2026-05-18T00:50:24.841332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.05941","created_at":"2026-05-18T00:50:24.841332+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZDXBA4AKKXYW","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZDXBA4AKKXYWTQPH","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZDXBA4AK","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ","json":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ.json","graph_json":"https://pith.science/api/pith-number/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/graph.json","events_json":"https://pith.science/api/pith-number/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/events.json","paper":"https://pith.science/paper/ZDXBA4AK"},"agent_actions":{"view_html":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ","download_json":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ.json","view_paper":"https://pith.science/paper/ZDXBA4AK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.05941&json=true","fetch_graph":"https://pith.science/api/pith-number/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/action/storage_attestation","attest_author":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/action/author_attestation","sign_citation":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/action/citation_signature","submit_replication":"https://pith.science/pith/ZDXBA4AKKXYWTQPHBPHTL6RIKJ/action/replication_record"}},"created_at":"2026-05-18T00:50:24.841332+00:00","updated_at":"2026-05-18T00:50:24.841332+00:00"}