{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3PXGZLBDTNAWUKXWGPLLAGJ52B","short_pith_number":"pith:3PXGZLBD","schema_version":"1.0","canonical_sha256":"dbee6cac239b416a2af633d6b0193dd067dbb821d9278c97e9f92336928247d1","source":{"kind":"arxiv","id":"2003.11213","version":1},"attestation_state":"computed","paper":{"title":"A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hongfeng You, Long Yu, Ning Xin, Shengwei Tian, Xiang Ma, Yan Xing","submitted_at":"2020-03-25T04:27:01Z","abstract_excerpt":"To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multiscale deconvolution decoder network and a pixel-level classification layer. In the network structure of the encoder, we use multiscale convolution instead of the traditional single-channel convolution. The multiple max-pooling integration mod"},"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":"2003.11213","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-25T04:27:01Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"434b6f925001030a765a9e60cdc337c63292ac29cad9866e3c41345e788347dd","abstract_canon_sha256":"3da455b11210135c71c557bfe52f74c09c8068f5c0872d7d40a5e03d13607966"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:32.203500Z","signature_b64":"hMrm0aMQyPhqHwZe19uKbCLG6owtTG+M0Ij87ZfyvJxR8mPa52kD5ZuowKjMuVjAHaoEFiOoGHPNAauwirwzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbee6cac239b416a2af633d6b0193dd067dbb821d9278c97e9f92336928247d1","last_reissued_at":"2026-07-05T00:50:32.203112Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:32.203112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hongfeng You, Long Yu, Ning Xin, Shengwei Tian, Xiang Ma, Yan Xing","submitted_at":"2020-03-25T04:27:01Z","abstract_excerpt":"To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multiscale deconvolution decoder network and a pixel-level classification layer. In the network structure of the encoder, we use multiscale convolution instead of the traditional single-channel convolution. The multiple max-pooling integration mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.11213","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2003.11213/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2003.11213","created_at":"2026-07-05T00:50:32.203168+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.11213v1","created_at":"2026-07-05T00:50:32.203168+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.11213","created_at":"2026-07-05T00:50:32.203168+00:00"},{"alias_kind":"pith_short_12","alias_value":"3PXGZLBDTNAW","created_at":"2026-07-05T00:50:32.203168+00:00"},{"alias_kind":"pith_short_16","alias_value":"3PXGZLBDTNAWUKXW","created_at":"2026-07-05T00:50:32.203168+00:00"},{"alias_kind":"pith_short_8","alias_value":"3PXGZLBD","created_at":"2026-07-05T00:50:32.203168+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/3PXGZLBDTNAWUKXWGPLLAGJ52B","json":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B.json","graph_json":"https://pith.science/api/pith-number/3PXGZLBDTNAWUKXWGPLLAGJ52B/graph.json","events_json":"https://pith.science/api/pith-number/3PXGZLBDTNAWUKXWGPLLAGJ52B/events.json","paper":"https://pith.science/paper/3PXGZLBD"},"agent_actions":{"view_html":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B","download_json":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B.json","view_paper":"https://pith.science/paper/3PXGZLBD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.11213&json=true","fetch_graph":"https://pith.science/api/pith-number/3PXGZLBDTNAWUKXWGPLLAGJ52B/graph.json","fetch_events":"https://pith.science/api/pith-number/3PXGZLBDTNAWUKXWGPLLAGJ52B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B/action/storage_attestation","attest_author":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B/action/author_attestation","sign_citation":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B/action/citation_signature","submit_replication":"https://pith.science/pith/3PXGZLBDTNAWUKXWGPLLAGJ52B/action/replication_record"}},"created_at":"2026-07-05T00:50:32.203168+00:00","updated_at":"2026-07-05T00:50:32.203168+00:00"}