{"doi":"10.1109/ojemb.2025.3624582","title":"Hierarchical Cross-Consistency Network Based Unsupervised Domain Adaptation for Pathology Whole Slide Image Segmentation","abstract":"Goal: Pathology images collected from different hospitals often have large appearance variability caused by different scanners, patients, or hospital protocols. Deep learning-based pathology segmentation models are highly dependent on the distribution of training data. Therefore, the models often suffer from the domain shift problem when applied to new target domains of different hospitals. Methods: To address this issue, we propose a hierarchical cross-consistency (HCC) network to hierarchically adapt models across pathology images of various domains with three consistency-based modules, the consistency module, the pair module, and the mixture module. The consistency module enhances the prediction consistency of each target image under various perturbations. The pair module improves consistency among different target images. Finally, the mixture module enhances the consistency across different domains. Results: The experimental results on pathology image datasets scanned using three different scanners show the superiority of the proposed HCC network compared to state-of-the-art unsupervised domain adaptation methods. Conclusions: The proposed method can successfully adapt trained pathology image segmentation models to new target domains, which is useful when introducing the models to different hospitals.","journal":"IEEE Open Journal of Engineering in Medicine and Biology","year":2025,"id":536959,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9478,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":605704,"name":"Weili Chen","orcid":"0000-0003-1998-5965","position":1,"is_corresponding":false},{"id":1422292,"name":"Chun-Rong Huang","orcid":"0000-0003-2372-5429","position":2,"is_corresponding":false},{"id":280670,"name":"Yang C. Fann","orcid":"0000-0003-2636-2002","position":3,"is_corresponding":false},{"id":249925,"name":"Lawrence L. Latour","orcid":"0000-0001-6160-5263","position":4,"is_corresponding":false},{"id":1422293,"name":"Pau‐Choo Chung","orcid":"0000-0002-8660-570X","position":5,"is_corresponding":false},{"id":1422291,"name":"Chien-Yu Chiou","orcid":"0000-0002-6737-2963","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:09.056872Z","pmid":null,"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":[]}