{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wen Z"],"funding":["NIDCR NIH HHS","NIAID NIH HHS","National Cancer Institute","NCI NIH HHS","National Institutes of Health","Welch Foundation","UT Southwestern Medical Center","NIGMS NIH HHS","Cancer Prevention and Research Institute of Texas"],"pagination":["100398"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11141889"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["37(2)"],"pubmed_abstract":["Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regi"],"journal":["Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc"],"pubmed_title":["Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images."],"pmcid":["PMC11141889"],"funding_grant_id":["1U01CA249245","F99 CA253718","I-1881","R01 DE030656","1R01GM140012","P30 CA142543","R01CA273585-01A1","P30CA142543","P50 CA070907","RP230330","R01 CA273585","R01CA207763","1R01DE030656","RP190610","U01 CA249245","K00CA253718","RP200327","R01 GM141519","RP180805","R01GM115473","R01 GM115473","1R01GM141519","U01 AI156189","R35 GM136375","P50CA70907","R01 GM140012","R01 CA207763"],"pubmed_authors":["Solis Soto LM","Wen Z","Brekken RA","Daoud EV","Xie Y","Wistuba II","Yang DM","Gu Z","Fujimoto J","Jia L","Behrens C","Luo D","Fang Y","Evers BM","Arner EN","Rong R","Wang S","O'Donnell KA","Lewis CM","Xiao G","Yang S"],"additional_accession":[]},"is_claimable":false,"name":"Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.","description":"Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regi","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2026-03-12T23:06:54.016Z","creation":"2025-08-12T03:05:01.689Z"},"accession":"S-EPMC11141889","cross_references":{"pubmed":["38043788"],"doi":["10.1016/j.modpat.2023.100398"]}}