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Our method leverages deep learning techniques to improve diagnostic accuracy and enhance patient treatment strategies. We developed a new dataset comprising 100 digitized whole-slide images of prostate needle core biopsy specimens, which are publicly available for research purposes. Our proposed model integrates dilated attention mechanisms and a residual convolutional U-Net architecture to enhance the richness of feature representations. Class imbalance is addressed using pixel expansion and class weights, and a five-fold cross-validation method ensures robust training and validation. In model ensemble evaluation, the model achieves an average Dice of 0.87 and accuracy of 0.92 on the cross-validation held-out folds. When applied to completely unseen, external test data, the model demonstrates an average Dice of 0.64 and accuracy of 0.81. Segmentation and grading results were validated by a team of expert pathologists. Based on experimental results, this study demonstrates the potential of our proposed method and model as a valuable tool for the detection and grading of prostate cancer in clinical settings.<\/jats:p>","DOI":"10.1371\/journal.pone.0331613","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:26:01Z","timestamp":1758317161000},"page":"e0331613","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection and score grading for prostate adenocarcinoma using semantic segmentation"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5342-7302","authenticated-orcid":true,"given":"Kasikrit","family":"Damkliang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paramee","family":"Thongsuksai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thakerng","family":"Wongsirichot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kanita","family":"Kayasut","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"issue":"3","key":"pone.0331613.ref001","first-page":"209","article-title":"Global Cancer Statistics 2020 : GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"H Sung","year":"2021","journal-title":"CA Cancer J Clin."},{"key":"pone.0331613.ref002","article-title":"Lyon, France: International Agency for Research on","author":"J Ferlay","year":"2024","journal-title":"Cancer."},{"issue":"1","key":"pone.0331613.ref003","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.eururo.2019.08.005","article-title":"Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates","volume":"77","author":"MB Culp","year":"2020","journal-title":"Eur Urol."},{"key":"pone.0331613.ref004","first-page":"1","article-title":"Current, Future Burden of Prostate Cancer in Songkhla, Thailand: Analysis of Incidence and Mortality Trends From 1990 to 2030","volume":"4","author":"CS Alvarez","year":"2018","journal-title":"J Glob Oncol."},{"issue":"4","key":"pone.0331613.ref005","doi-asserted-by":"crossref","first-page":"461","DOI":"10.5858\/arpa.2020-0015-RA","article-title":"The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer","volume":"145","author":"JI Epstein","year":"2021","journal-title":"Arch Pathol Lab Med."},{"issue":"1","key":"pone.0331613.ref006","first-page":"95","article-title":"Histopathological Study of the Prostate Cancer Growth Patterns in Relation with the Grading Systems","volume":"48","author":"TCT Popescu","year":"2022","journal-title":"Curr Health Sci J."},{"key":"pone.0331613.ref007","doi-asserted-by":"crossref","first-page":"128613","DOI":"10.1109\/ACCESS.2020.3008868","article-title":"PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection","volume":"8","author":"L Duran-Lopez","year":"2020","journal-title":"IEEE Access."},{"key":"pone.0331613.ref008","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. 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