{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:18:23Z","timestamp":1787980703935,"version":"build-2784847793"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"crossref","award":["075-15-2024-529"],"award-info":[{"award-number":["075-15-2024-529"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Estrogen receptor immunohistochemistry (ER-IHC) exhibits heterogeneous staining and substantial class imbalance, complicating segmentation of ordered expression categories. We evaluated whether class weighting, minority-focused crop sampling, adaptive minority curriculum (AMC), and Focal\u2013Tversky optimization improved a common ResUNet-DS backbone for segmenting background\/non-target pixels and C1 ER-negative, C2 weak-positive, C3 moderate-positive, and C4 strong-positive foreground categories. Methods: The dataset comprised 220 paired 512\u00d7512 image\u2013mask patches organized into 44 recovered five-patch source groups. A source-group-preserving nested five-fold design used outer folds of 45, 45, 45, 45, and 40 patches. Six controlled training conditions were compared, with four independently selected inner models ensembled for each condition and outer fold. Results: Unweighted random training achieved the best numerical mean for all five primary endpoints: foreground Dice (0.7479\u00b10.0123), C2\u2013C4 Dice (0.7098\u00b10.0128), foreground IoU (0.6074\u00b10.0147), foreground quadratically weighted kappa (0.9761\u00b10.0040), and foreground-ordinal MAE (0.0446\u00b10.0062). Weighted random training was the strongest weighted\/minority-sensitive condition but did not exceed the unweighted reference. None of 25 paired outer-fold comparisons reached p&lt;0.05; the minimum raw p-value was 0.0625, and all Holm-adjusted p-values were 0.3125. A 20,000-replicate paired source-group bootstrap preserved the same overall ordering. Conclusions: More complex imbalance-handling strategies did not improve aggregate segmentation over unweighted training. Post hoc analyses showed scope-dependent probability quality and error ranking. IHC4BC provided expression-ordering consistency but not direct external segmentation validation. The findings support preliminary source-group-preserving methodological evidence rather than patient-level, whole-slide, or clinical validity.<\/jats:p>","DOI":"10.3390\/diagnostics16172752","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:13Z","timestamp":1787849233000},"page":"2752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward AI-Assisted Precision Diagnostics in Breast Cancer: Source-Group-Preserving Evaluation of Class-Imbalance Strategies for Ordered ER-IHC Segmentation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6112-7673","authenticated-orcid":false,"given":"Md Saiful","family":"Arefin","sequence":"first","affiliation":[{"name":"School of Computer Science and Informatics, Department of Computer Science, Albukhary International University, Alor Setar 05200, Kedah, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1405-2892","authenticated-orcid":false,"given":"Mohammad Saiful","family":"Islam","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Department of Computer Science, Albukhary International University, Alor Setar 05200, Kedah, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Serajun","family":"Nabi","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3610-2562","authenticated-orcid":false,"given":"Rashadul Islam","family":"Sumon","sequence":"additional","affiliation":[{"name":"Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-9142","authenticated-orcid":false,"given":"Maria","family":"Lapina","sequence":"additional","affiliation":[{"name":"Trusted AI Research Center, Ivannikov Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia"},{"name":"Department of Computational Mathematics and Cybernetics, North-Caucasus Federal University, 355017 Stavropol, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0611-7002","authenticated-orcid":false,"given":"Vitalii","family":"Lapin","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics and Cybernetics, North-Caucasus Federal University, 355017 Stavropol, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1165-7812","authenticated-orcid":false,"given":"Mohammed","family":"Muthanna","sequence":"additional","affiliation":[{"name":"Faculty of Computing and IT, Sohar University, Sohar 311, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fauzi, M.F.A., Wan Ahmad, W.S.H.M., Jamaluddin, M.F., Lee, J.T.H., Khor, S.Y., Looi, L.M., Abas, F.S., and Aldahoul, N. (2022). Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123093"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116471","DOI":"10.1016\/j.eswa.2021.116471","article-title":"Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images","volume":"193","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhen, T., Fu, Y., Wang, Y., He, Y., Han, A., and Shi, H. (2024). AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images. Cancers, 16.","DOI":"10.3390\/cancers16010167"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109044","DOI":"10.1016\/j.bspc.2025.109044","article-title":"IHC-IS4BC: A supervised dataset for pathological image segmentation based on IHC-staining slides of breast cancer","volume":"113","author":"Li","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.compmedimag.2021.102026","article-title":"Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation","volume":"95","author":"Yeung","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_6","unstructured":"Akbarnejad, A., Ray, N., Barnes, P.J., and Bigras, G. (2023). Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111941","DOI":"10.1016\/j.dib.2025.111941","article-title":"HER2-SISH40x: Annotated histopathology image dataset for HER2 amplification assessment in breast cancer","volume":"63","author":"Rehman","year":"2025","journal-title":"Data Brief"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/his.14820","article-title":"Artificial intelligence in breast cancer histopathology","volume":"82","author":"Chan","year":"2023","journal-title":"Histopathology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105819","DOI":"10.1016\/j.humpath.2025.105819","article-title":"Artificial intelligence in breast pathology: Overview and recent updates","volume":"162","author":"Datwani","year":"2025","journal-title":"Hum. Pathol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1474","DOI":"10.1002\/widm.1474","article-title":"A survey on artificial intelligence in histopathology image analysis","volume":"12","author":"Abdelsamea","year":"2022","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.csbj.2024.12.033","article-title":"Machine learning methods for histopathological image analysis: Updates in 2024","volume":"27","author":"Komura","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100363","DOI":"10.1016\/j.jpi.2024.100363","article-title":"Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review","volume":"15","author":"Tafavvoghi","year":"2024","journal-title":"J. Pathol. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104791","DOI":"10.1016\/j.bspc.2023.104791","article-title":"Transformers in medical image segmentation: A review","volume":"84","author":"Xiao","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103061","DOI":"10.1016\/j.media.2023.103061","article-title":"Segment anything model for medical images?","volume":"92","author":"Huang","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: An experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102473","DOI":"10.1016\/j.compmedimag.2024.102473","article-title":"A review of the Segment Anything Model (SAM) for medical image analysis: Accomplishments and perspectives","volume":"119","author":"Ali","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108238","DOI":"10.1016\/j.compbiomed.2024.108238","article-title":"Segment anything model for medical image segmentation: Current applications and future directions","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., and Belongie, S. (2019). Class-Balanced Loss Based on Effective Number of Samples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102035","DOI":"10.1016\/j.media.2021.102035","article-title":"Loss odyssey in medical image segmentation","volume":"71","author":"Ma","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1093\/jamia\/ocac093","article-title":"The Harm of Class Imbalance Corrections for Risk Prediction Models: Illustration and Simulation Using Logistic Regression","volume":"29","author":"Timmerman","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_27","first-page":"1321","article-title":"On Calibration of Modern Neural Networks","volume":"Volume 70","author":"Guo","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6\u201311 August 2017"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/JAS.2022.105668","article-title":"Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels","volume":"9","author":"Lei","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hoebel, K.V., Lemay, A., Campbell, J.P., Ostmo, S., Chiang, M.F., Bridge, C.P., Li, M.D., Singh, P., Coyner, A.S., and Kalpathy-Cramer, J. (2026). Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging. PLoS Digit. Health, 5.","DOI":"10.1371\/journal.pdig.0001248"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Litvinov, A., Ushakov, E., Senotrusova, S., Lukianov, K., Markin, Y., Mikhailova, L., and Karpulevich, E. (2026). Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers. J. Clin. Med., 15.","DOI":"10.3390\/jcm15010365"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100003","DOI":"10.1016\/j.metrad.2023.100003","article-title":"A review of uncertainty estimation and its application in medical imaging","volume":"1","author":"Zou","year":"2023","journal-title":"Meta-Radiology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103223","DOI":"10.1016\/j.media.2024.103223","article-title":"A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods","volume":"97","author":"Huang","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102574","DOI":"10.1016\/j.compmedimag.2025.102574","article-title":"Evaluation of uncertainty estimation methods in medical image segmentation: Exploring the usage of uncertainty in clinical deployment","volume":"124","author":"Li","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103602","DOI":"10.1016\/j.media.2025.103602","article-title":"Structural uncertainty estimation for medical image segmentation","volume":"103","author":"Yang","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6572","DOI":"10.1038\/s41467-022-34025-x","article-title":"Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology","volume":"13","author":"Dolezal","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_36","unstructured":"Ahmad, W., Munirah, W.S.H., Fauzi, M.F.A., Hasan, M.J., Rehman, Z.U., Lee, J.T.H., Khor, S.Y., Looi, L.M., Abas, F.S., and Adam, A. (2023). UMMC ER-IHC Breast Histopathology Whole Slide Image and Allred Score, Zenodo."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ullah, M.H., Hasan, M.J., Wan Ahmad, W.S.H.M., Ahmad Fauzi, M.F., Rehman, Z.U., Lee, J.T.H., Khor, S.Y., and Looi, L.M. (2025). Complete Workflow for ER-IHC Pathology Database Revalidation. AI, 6.","DOI":"10.3390\/ai6090204"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2752\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T04:34:52Z","timestamp":1787978092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,27]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16172752"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16172752","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,27]]}}}