{"doi":"10.1016/j.eswa.2020.113387","title":"Automatic segmentation of whole-slide H&amp;E stained breast histopathology images using a deep convolutional neural network architecture","abstract":null,"journal":"Expert Systems with Applications","year":2020,"id":659639,"datarank":0.6476232170304466,"base_score":4.31748811353631,"endowment":4.31748811353631,"self_citation_contribution":0.6476232170304466,"citation_network_contribution":0.0,"self_endowment_contribution":0.6476232170304466,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":74,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1721931,"name":"Daniel Sanchez-Morillo","orcid":"0000-0001-5603-0936","position":1,"is_corresponding":false},{"id":1721932,"name":"Miguel Angel Fernandez-Granero","orcid":null,"position":2,"is_corresponding":false},{"id":1721933,"name":"Marcial Garcia-Rojo","orcid":null,"position":3,"is_corresponding":false},{"id":1721930,"name":"Blanca Maria Priego-Torres","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Automatic segmentation of whole-slide H&amp;E stained breast histopathology images using a deep convolutional neural network architecture","abstract":"<p>In this research, we propose a processing pipeline for the automatic segmentation of stained BC images presenting different types of histopathological patterns. Experimental results on a collection of patches of breast cancer images demonstrate how the designed processing pipeline performs properly regardless of the size, texture or any other colour-shape features typical of the malignant carcinomas considered in this study. The estimated segmentation accuracy and frequency-weighted intersection over union ( FWIoU ) were 95.62%, 92.52%, respectively. Additionally, a web-based platform which includes a slide-viewer and an annotation tool was developed. The automatic segmentation method proposed in this work was integrated into this platform and currently, it is being used as a decision-support tool by pathologists.</p>","is_dataset_classified":null,"base_score":4.31748811353631,"endowment":4.31748811353631,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W3011941780","authors":[],"funders":[{"funder_name":"Fondo de Desarrollo Regional","grant_id":"PI-0032-2017","title":"Subvención para la financiación de la investigación y la innovación biomédica y en Ciencias de la Salud en el marco de la iniciativa territorial integrada 2014–2020 para la provincia de Cádiz."}],"total_grants":1,"fwci":6.5678,"citation_percentile":0.9729217,"influential_citations":0,"citation_trend":[{"year":2020,"count":2},{"year":2021,"count":14},{"year":2022,"count":19},{"year":2023,"count":14},{"year":2024,"count":16},{"year":2025,"count":5},{"year":2026,"count":4}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"http://www.sciencedirect.com/science/article/pii/S0957417420302116","host_type":"repository"},{"url":"http://www.sciencedirect.com/science/article/pii/S0957417420302116","host_type":"repository"},{"url":"https://api.elsevier.com/content/article/PII:S0957417420302116?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0957417420302116?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.eswa.2020.113387","host_type":"journal"},{"url":"http://hdl.handle.net/10498/30087","host_type":"repository"}],"fields_of_study":["AI in cancer detection","Radiomics and Machine Learning in Medical Imaging","Medical Image Segmentation Techniques"],"mesh_terms":[],"keywords":["Computer science","Artificial intelligence","Segmentation","Convolutional neural network","Pattern recognition (psychology)","Pipeline (software)","Deep learning","Computer vision","Image segmentation","Breast cancer","H&E staining","Whole-Slide Imaging"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T07:20:07.368968Z","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":[]}