<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Wen Z</submitter><funding>NIDCR NIH HHS</funding><funding>NIAID NIH HHS</funding><funding>National Cancer Institute</funding><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><funding>Welch Foundation</funding><funding>UT Southwestern Medical Center</funding><funding>NIGMS NIH HHS</funding><funding>Cancer Prevention and Research Institute of Texas</funding><pagination>100398</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11141889</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>37(2)</volume><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</pubmed_abstract><journal>Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc</journal><pubmed_title>Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.</pubmed_title><pmcid>PMC11141889</pmcid><funding_grant_id>1U01CA249245</funding_grant_id><funding_grant_id>F99 CA253718</funding_grant_id><funding_grant_id>I-1881</funding_grant_id><funding_grant_id>R01 DE030656</funding_grant_id><funding_grant_id>1R01GM140012</funding_grant_id><funding_grant_id>P30 CA142543</funding_grant_id><funding_grant_id>R01CA273585-01A1</funding_grant_id><funding_grant_id>P30CA142543</funding_grant_id><funding_grant_id>P50 CA070907</funding_grant_id><funding_grant_id>RP230330</funding_grant_id><funding_grant_id>R01 CA273585</funding_grant_id><funding_grant_id>R01CA207763</funding_grant_id><funding_grant_id>1R01DE030656</funding_grant_id><funding_grant_id>RP190610</funding_grant_id><funding_grant_id>U01 CA249245</funding_grant_id><funding_grant_id>K00CA253718</funding_grant_id><funding_grant_id>RP200327</funding_grant_id><funding_grant_id>R01 GM141519</funding_grant_id><funding_grant_id>RP180805</funding_grant_id><funding_grant_id>R01GM115473</funding_grant_id><funding_grant_id>R01 GM115473</funding_grant_id><funding_grant_id>1R01GM141519</funding_grant_id><funding_grant_id>U01 AI156189</funding_grant_id><funding_grant_id>R35 GM136375</funding_grant_id><funding_grant_id>P50CA70907</funding_grant_id><funding_grant_id>R01 GM140012</funding_grant_id><funding_grant_id>R01 CA207763</funding_grant_id><pubmed_authors>Solis Soto LM</pubmed_authors><pubmed_authors>Wen Z</pubmed_authors><pubmed_authors>Brekken RA</pubmed_authors><pubmed_authors>Daoud EV</pubmed_authors><pubmed_authors>Xie Y</pubmed_authors><pubmed_authors>Wistuba II</pubmed_authors><pubmed_authors>Yang DM</pubmed_authors><pubmed_authors>Gu Z</pubmed_authors><pubmed_authors>Fujimoto J</pubmed_authors><pubmed_authors>Jia L</pubmed_authors><pubmed_authors>Behrens C</pubmed_authors><pubmed_authors>Luo D</pubmed_authors><pubmed_authors>Fang Y</pubmed_authors><pubmed_authors>Evers BM</pubmed_authors><pubmed_authors>Arner EN</pubmed_authors><pubmed_authors>Rong R</pubmed_authors><pubmed_authors>Wang S</pubmed_authors><pubmed_authors>O'Donnell KA</pubmed_authors><pubmed_authors>Lewis CM</pubmed_authors><pubmed_authors>Xiao G</pubmed_authors><pubmed_authors>Yang S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.</name><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</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Feb</publication><modification>2026-03-12T23:06:54.016Z</modification><creation>2025-08-12T03:05:01.689Z</creation></dates><accession>S-EPMC11141889</accession><cross_references><pubmed>38043788</pubmed><doi>10.1016/j.modpat.2023.100398</doi></cross_references></HashMap>