{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2Q46GRH6VMLQCSFKTPTCERIDLV","short_pith_number":"pith:2Q46GRH6","schema_version":"1.0","canonical_sha256":"d439e344feab170148aa9be62245035d64a018bb9dff4a6761ae3ce16cd2bdfb","source":{"kind":"arxiv","id":"2403.03849","version":5},"attestation_state":"computed","paper":{"title":"MedMamba: Vision Mamba for Medical Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Yubiao Yue, Zhenzhang Li","submitted_at":"2024-03-06T16:49:33Z","abstract_excerpt":"Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model l"},"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":"2403.03849","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-03-06T16:49:33Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"9215ddd054504222cb3bdf4d8ce53ffd9aa0f0054a9675e4b0b43097d06990a8","abstract_canon_sha256":"daa09693f5c2251f5f982102ff93e835fee6433d7130b6124378c9de90be58ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:00.059079Z","signature_b64":"yMnhIsl9F3pQtklyDRj8wLENFHK6ksYnwMDa5B1/koqIucQSV4aL7TI3KCh24CUkxH8LUOf3Z/QAwGL20oh9AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d439e344feab170148aa9be62245035d64a018bb9dff4a6761ae3ce16cd2bdfb","last_reissued_at":"2026-07-05T09:13:00.058584Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:00.058584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedMamba: Vision Mamba for Medical Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Yubiao Yue, Zhenzhang Li","submitted_at":"2024-03-06T16:49:33Z","abstract_excerpt":"Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03849","kind":"arxiv","version":5},"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/2403.03849/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":"2403.03849","created_at":"2026-07-05T09:13:00.058645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03849v5","created_at":"2026-07-05T09:13:00.058645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03849","created_at":"2026-07-05T09:13:00.058645+00:00"},{"alias_kind":"pith_short_12","alias_value":"2Q46GRH6VMLQ","created_at":"2026-07-05T09:13:00.058645+00:00"},{"alias_kind":"pith_short_16","alias_value":"2Q46GRH6VMLQCSFK","created_at":"2026-07-05T09:13:00.058645+00:00"},{"alias_kind":"pith_short_8","alias_value":"2Q46GRH6","created_at":"2026-07-05T09:13:00.058645+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05311","citing_title":"Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation","ref_index":122,"is_internal_anchor":true},{"citing_arxiv_id":"2606.20738","citing_title":"An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14799","citing_title":"Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12929","citing_title":"Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22547","citing_title":"Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00489","citing_title":"3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":226,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22547","citing_title":"Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18130","citing_title":"Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2507.05193","citing_title":"RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2511.11030","citing_title":"Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12929","citing_title":"Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05616","citing_title":"RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12437","citing_title":"A Hybrid Architecture for Benign-Malignant Classification of Mammography ROIs","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12999","citing_title":"Agentic Discovery with Active Hypothesis Exploration for Visual Recognition","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV","json":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV.json","graph_json":"https://pith.science/api/pith-number/2Q46GRH6VMLQCSFKTPTCERIDLV/graph.json","events_json":"https://pith.science/api/pith-number/2Q46GRH6VMLQCSFKTPTCERIDLV/events.json","paper":"https://pith.science/paper/2Q46GRH6"},"agent_actions":{"view_html":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV","download_json":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV.json","view_paper":"https://pith.science/paper/2Q46GRH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03849&json=true","fetch_graph":"https://pith.science/api/pith-number/2Q46GRH6VMLQCSFKTPTCERIDLV/graph.json","fetch_events":"https://pith.science/api/pith-number/2Q46GRH6VMLQCSFKTPTCERIDLV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV/action/storage_attestation","attest_author":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV/action/author_attestation","sign_citation":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV/action/citation_signature","submit_replication":"https://pith.science/pith/2Q46GRH6VMLQCSFKTPTCERIDLV/action/replication_record"}},"created_at":"2026-07-05T09:13:00.058645+00:00","updated_at":"2026-07-05T09:13:00.058645+00:00"}