{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Y26WW7KANDILGS2VE72NQMYT2H","short_pith_number":"pith:Y26WW7KA","schema_version":"1.0","canonical_sha256":"c6bd6b7d4068d0b34b5527f4d83313d1eff84288154f8d5819a20f91a0f265f3","source":{"kind":"arxiv","id":"1904.00625","version":4},"attestation_state":"computed","paper":{"title":"Med3D: Transfer Learning for 3D Medical Image Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Ma, Sihong Chen, Yefeng Zheng","submitted_at":"2019-04-01T08:14:29Z","abstract_excerpt":"The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities"},"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":"1904.00625","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-01T08:14:29Z","cross_cats_sorted":[],"title_canon_sha256":"6168300d61b3900330bc93dcb50e9ca07bf4cb1450830f0faa28dfd6a332ef45","abstract_canon_sha256":"df88a71181c331dca7705156e444d18cba2a689716bd6d4b2c7ef9184c75d4f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:23.527054Z","signature_b64":"5JuXANYaz0NH2lTkP21zcauN4Ee5nqq/lsCO9fimk5nZXf08mRERqspYma07/osveu32c+hVTlA5I3Iug+JXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6bd6b7d4068d0b34b5527f4d83313d1eff84288154f8d5819a20f91a0f265f3","last_reissued_at":"2026-05-17T23:40:23.526379Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:23.526379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Med3D: Transfer Learning for 3D Medical Image Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Ma, Sihong Chen, Yefeng Zheng","submitted_at":"2019-04-01T08:14:29Z","abstract_excerpt":"The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00625","kind":"arxiv","version":4},"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":"1904.00625","created_at":"2026-05-17T23:40:23.526479+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00625v4","created_at":"2026-05-17T23:40:23.526479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00625","created_at":"2026-05-17T23:40:23.526479+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y26WW7KANDIL","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y26WW7KANDILGS2V","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y26WW7KA","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2406.12632","citing_title":"Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2411.05824","citing_title":"Navigating Distribution Shifts in Medical Image Analysis: A Survey","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19060","citing_title":"LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2604.22700","citing_title":"Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05522","citing_title":"Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00718","citing_title":"Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11348","citing_title":"LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07329","citing_title":"Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06859","citing_title":"Knowledge Transfer Scaling Laws for 3D Medical Imaging","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14506","citing_title":"Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04705","citing_title":"Vol-Mark: A Watermark for 3D Medical Volume Data Via Cubic Difference Expansion and Contrastive Learning","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H","json":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H.json","graph_json":"https://pith.science/api/pith-number/Y26WW7KANDILGS2VE72NQMYT2H/graph.json","events_json":"https://pith.science/api/pith-number/Y26WW7KANDILGS2VE72NQMYT2H/events.json","paper":"https://pith.science/paper/Y26WW7KA"},"agent_actions":{"view_html":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H","download_json":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H.json","view_paper":"https://pith.science/paper/Y26WW7KA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00625&json=true","fetch_graph":"https://pith.science/api/pith-number/Y26WW7KANDILGS2VE72NQMYT2H/graph.json","fetch_events":"https://pith.science/api/pith-number/Y26WW7KANDILGS2VE72NQMYT2H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H/action/storage_attestation","attest_author":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H/action/author_attestation","sign_citation":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H/action/citation_signature","submit_replication":"https://pith.science/pith/Y26WW7KANDILGS2VE72NQMYT2H/action/replication_record"}},"created_at":"2026-05-17T23:40:23.526479+00:00","updated_at":"2026-05-17T23:40:23.526479+00:00"}