{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JCHFAUURBQJIKQGEDX6JVEFN74","short_pith_number":"pith:JCHFAUUR","schema_version":"1.0","canonical_sha256":"488e5052910c128540c41dfc9a90adff1a6541a55a1afd96fcd73679a3039a0f","source":{"kind":"arxiv","id":"1810.05735","version":1},"attestation_state":"computed","paper":{"title":"InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhijit Guha Roy, Christian Wachinger, Nassir Navab, Sailesh Conjeti, Shubham Kumar","submitted_at":"2018-10-11T16:05:00Z","abstract_excerpt":"We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities. InfiNet consists of double encoder arms for T1 and T2 input scans that feed into a joint-decoder arm that terminates in the classification layer. The novelty of InfiNet lies in the manner in which the decoder upsamples lower resolution input feature map(s) from multiple encoder arms. Specifically, the p"},"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":"1810.05735","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-11T16:05:00Z","cross_cats_sorted":[],"title_canon_sha256":"a4d8007c76804d36f20d2dca1bffb93e3798f8eb89b0a711a11b663b7940df99","abstract_canon_sha256":"654b77defbc979bdb2eb6a9177232eec34ef3a7953bacab3ef7d05d70440bf7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:27.083768Z","signature_b64":"FMiYM2yz+w5r/Ah4KC4ndR9J5pFekcc1BdGSPkw/1/id+h/v66GmblHeGR1Nmg3Yeaty/P6JXoh8MUacl0MgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"488e5052910c128540c41dfc9a90adff1a6541a55a1afd96fcd73679a3039a0f","last_reissued_at":"2026-05-18T00:03:27.083360Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:27.083360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhijit Guha Roy, Christian Wachinger, Nassir Navab, Sailesh Conjeti, Shubham Kumar","submitted_at":"2018-10-11T16:05:00Z","abstract_excerpt":"We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities. InfiNet consists of double encoder arms for T1 and T2 input scans that feed into a joint-decoder arm that terminates in the classification layer. The novelty of InfiNet lies in the manner in which the decoder upsamples lower resolution input feature map(s) from multiple encoder arms. Specifically, the p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.05735","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":"1810.05735","created_at":"2026-05-18T00:03:27.083429+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.05735v1","created_at":"2026-05-18T00:03:27.083429+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.05735","created_at":"2026-05-18T00:03:27.083429+00:00"},{"alias_kind":"pith_short_12","alias_value":"JCHFAUURBQJI","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JCHFAUURBQJIKQGE","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JCHFAUUR","created_at":"2026-05-18T12:32:31.084164+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/JCHFAUURBQJIKQGEDX6JVEFN74","json":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74.json","graph_json":"https://pith.science/api/pith-number/JCHFAUURBQJIKQGEDX6JVEFN74/graph.json","events_json":"https://pith.science/api/pith-number/JCHFAUURBQJIKQGEDX6JVEFN74/events.json","paper":"https://pith.science/paper/JCHFAUUR"},"agent_actions":{"view_html":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74","download_json":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74.json","view_paper":"https://pith.science/paper/JCHFAUUR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.05735&json=true","fetch_graph":"https://pith.science/api/pith-number/JCHFAUURBQJIKQGEDX6JVEFN74/graph.json","fetch_events":"https://pith.science/api/pith-number/JCHFAUURBQJIKQGEDX6JVEFN74/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74/action/storage_attestation","attest_author":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74/action/author_attestation","sign_citation":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74/action/citation_signature","submit_replication":"https://pith.science/pith/JCHFAUURBQJIKQGEDX6JVEFN74/action/replication_record"}},"created_at":"2026-05-18T00:03:27.083429+00:00","updated_at":"2026-05-18T00:03:27.083429+00:00"}