{"doi":"10.1016/j.media.2024.103124","title":"Cross-scale multi-instance learning for pathological image diagnosis","abstract":"Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.","journal":"Medical Image Analysis","year":2024,"id":417055,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":84,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9551,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1037188,"name":"Can Cui","orcid":"0000-0002-2159-5387","position":1,"is_corresponding":false},{"id":737882,"name":"Lucas W. Remedios","orcid":null,"position":2,"is_corresponding":false},{"id":425888,"name":"Shunxing Bao","orcid":"0000-0001-6376-4292","position":3,"is_corresponding":false},{"id":1037190,"name":"R. Michael Womick","orcid":"0000-0003-3429-9971","position":4,"is_corresponding":false},{"id":1162875,"name":"Sophie Chiron","orcid":null,"position":5,"is_corresponding":false},{"id":500752,"name":"Jia Li","orcid":"0000-0002-4969-7158","position":6,"is_corresponding":false},{"id":86856,"name":"Joseph T. Roland","orcid":"0000-0001-9977-0520","position":7,"is_corresponding":false},{"id":60556,"name":"Ken S. Lau","orcid":"0000-0001-8438-0319","position":8,"is_corresponding":false},{"id":925874,"name":"Qi Liu","orcid":"0000-0001-5378-6404","position":9,"is_corresponding":false},{"id":60566,"name":"Keith T. Wilson","orcid":"0000-0003-4421-1830","position":10,"is_corresponding":false},{"id":1202417,"name":"Yaohong Wang","orcid":"0000-0001-5964-0465","position":11,"is_corresponding":false},{"id":60562,"name":"Lori A. Coburn","orcid":"0000-0003-3249-9806","position":12,"is_corresponding":false},{"id":104551,"name":"Bennett A. Landman","orcid":"0000-0001-5733-2127","position":13,"is_corresponding":false},{"id":291228,"name":"Yuankai Huo","orcid":"0000-0002-2096-8065","position":14,"is_corresponding":false},{"id":643160,"name":"Ruining Deng","orcid":"0000-0001-6300-8518","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T01:56:50.743370Z","pmid":"38428271","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}