{"doi":"10.1016/j.ebiom.2023.104769","title":"Self-supervised deep learning for highly efficient spatial immunophenotyping","abstract":"BACKGROUND: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. METHODS: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. FINDINGS: With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. INTERPRETATION: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. FUNDING: This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.","journal":"EBioMedicine","year":2023,"id":362771,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.958,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":235930,"name":"Khalid AbdulJabbar","orcid":"0000-0002-4411-4435","position":1,"is_corresponding":false},{"id":1116776,"name":"Tami Grunewald","orcid":null,"position":2,"is_corresponding":false},{"id":235933,"name":"Ayse U. Akarca","orcid":"0000-0003-0629-3927","position":3,"is_corresponding":false},{"id":284905,"name":"Yeman Brhane Hagos","orcid":"0000-0002-0357-6297","position":4,"is_corresponding":false},{"id":686261,"name":"Faranak Sobhani","orcid":"0000-0002-1787-4455","position":5,"is_corresponding":false},{"id":1074859,"name":"Catherine S.Y. Lecat","orcid":"0000-0001-9233-6967","position":6,"is_corresponding":false},{"id":1074860,"name":"Dominic Patel","orcid":"0000-0001-9223-6632","position":7,"is_corresponding":false},{"id":856646,"name":"Lydia Lee","orcid":"0000-0002-6092-8949","position":8,"is_corresponding":false},{"id":259745,"name":"Manuel Rodriguez‐Justo","orcid":"0000-0001-5007-1761","position":9,"is_corresponding":false},{"id":250471,"name":"Kwee Yong","orcid":"0000-0002-6487-276X","position":10,"is_corresponding":false},{"id":406840,"name":"Jonathan A. Ledermann","orcid":"0000-0003-3799-3539","position":11,"is_corresponding":false},{"id":17923,"name":"John Le Quesne","orcid":"0000-0003-3552-7446","position":12,"is_corresponding":false},{"id":225107,"name":"E. Shelley Hwang","orcid":"0000-0002-8571-1148","position":13,"is_corresponding":false},{"id":235940,"name":"Teresa Marafioti","orcid":"0000-0003-1223-6275","position":14,"is_corresponding":false},{"id":235942,"name":"Yinyin Yuan","orcid":"0000-0002-8556-4707","position":15,"is_corresponding":false},{"id":1033410,"name":"Hanyun Zhang","orcid":"0000-0002-4140-7881","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:14:23.749325Z","pmid":"37672979","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":[]}