{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:26:06Z","timestamp":1777983966787,"version":"3.51.4"},"reference-count":38,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"crossref","award":["2022CFA007"],"award-info":[{"award-number":["2022CFA007"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019034","name":"Hubei Provincial Central Guidance Local Science and Technology Development Project","doi-asserted-by":"crossref","award":["2023EGA027"],"award-info":[{"award-number":["2023EGA027"]}],"id":[{"id":"10.13039\/501100019034","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Breast cancer poses a significant threat to women\u2019s health, and early diagnosis is crucial for reducing mortality rates. Automatic breast tumor segmentation is important in medical image processing, but existing methods face challenges with breast pathology images due to sample scarcity, image degradation after data augmentation, and limitations in feature extraction. Traditional networks like U-Net often lose small lesions and edge details during downsampling and struggle with complex images and class imbalance. To address these issues, this study proposes DetailEdgeSkipBalance-Net (DESB-Net), an improved segmentation model based on U-Net. DESB-Net includes several innovations: Enhanced Detail-aware Multi-scale Re-parameterized Convolution (EDConv) for enhanced feature extraction, HistoEdge Focus Module (HEFM) for edge enhancement, Multi-Path Fusion Module (MPFM) for multi-scale feature fusion, and Binary Cross-Entropy Dice Loss (BD Loss) to balance class imbalance and boundary accuracy. These improvements significantly enhance the model\u2019s ability to capture small lesions and edge details, improve segmentation accuracy and robustness, and maintain high computational efficiency. On the UCSB dataset, DESB-Net achieved an mIoU of 79.53% and accuracy of 97.02%, outperforming U-Net by 6.5% and 1.89%, respectively, without increasing parameters or computational load. On the BCSS dataset, it achieved an mIoU of 63.4% and accuracy of 85.8%, surpassing U-Net by 4.2% and 2.6%. DESB-Net also outperformed mainstream models like DeepLabv3+, SegFormer, ResUNet, and Connected-UNets, demonstrating its effectiveness in breast pathology image segmentation. These results highlight the potential of DESB-Net to improve diagnostic accuracy and efficiency in clinical settings, making it a promising tool for early detection and treatment of breast cancer.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae524e","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T22:47:55Z","timestamp":1773701275000},"page":"025051","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Breast pathology image segmentation based on DESB-net: a fusion strategy of detail enhancement, edge focus, and cross-layer connections"],"prefix":"10.1088","volume":"12","author":[{"given":"Min","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8900-4867","authenticated-orcid":true,"given":"Jianda","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benhui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengbo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"bpexae524ebib1","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1038\/s41591-025-03502-3","type":"journal-article","article-title":"Global patterns and trends in breast cancer incidence and mortality across 185 countries","volume":"31","author":"Kim","year":"2025","journal-title":"Nat. Med."},{"key":"bpexae524ebib2","doi-asserted-by":"publisher","DOI":"10.1101\/2022.10.06.510405","type":"journal-article","article-title":"High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of ffpe tissue","author":"Janesick","year":"2022","journal-title":"bioRxiv"},{"key":"bpexae524ebib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/1994764","type":"journal-article","article-title":"Review of breast cancer pathologigcal image processing","volume":"2021","author":"Zhang","year":"2021","journal-title":"BioMed Research International"},{"key":"bpexae524ebib4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.asoc.2019.105765","type":"journal-article","article-title":"Computer-aided diagnosis system combining fcn and bi-lstm model for efficient breast cancer detection from histopathological images","volume":"85","author":"Budak","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"bpexae524ebib5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101657","type":"journal-article","article-title":"Segmentation of breast ultrasound image with semantic classification of superpixels","volume":"61","author":"Huang","year":"2020","journal-title":"Med. Image Anal."},{"key":"bpexae524ebib6","doi-asserted-by":"publisher","first-page":"102481","DOI":"10.1016\/j.bspc.2021.102481","type":"journal-article","article-title":"Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using unet model based-semantic segmentation","volume":"66","author":"Khaoula Belhaj Soulami","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"bpexae524ebib7","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000015200","type":"journal-article","article-title":"Detection and classification the breast tumors using mask r-cnn on sonograms","volume":"98","author":"Chiao","year":"2019","journal-title":"Medicine"},{"key":"bpexae524ebib8","doi-asserted-by":"publisher","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","type":"journal-article","article-title":"Medical image segmentation review: the success of u-net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"bpexae524ebib9","doi-asserted-by":"publisher","author":"Ronneberger","year":"2015","DOI":"10.1007\/978-3-319-24574-4_28","type":"book"},{"key":"bpexae524ebib10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2204.08470","type":"journal-article","article-title":"U-net and its variants for medical image segmentation : a short review","author":"Ummadi","year":"2022","journal-title":"arXiv"},{"key":"bpexae524ebib11","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/JBHI.2020.2996783","type":"journal-article","article-title":"Mi-unet: Multi-inputs unet incorporating brain parcellation for stroke lesion segmentation from t1-weighted magnetic resonance images","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"bpexae524ebib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jvcir.2015.05.003","type":"journal-article","article-title":"Rotation invariant color texture classification using multiple sub-dlbps","volume":"31","author":"Lian","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"bpexae524ebib13","doi-asserted-by":"publisher","first-page":"67504","DOI":"10.1109\/ACCESS.2021.3073051","type":"journal-article","article-title":"Resd-unet research and application for pulmonary artery segmentation","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"bpexae524ebib14","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/ACCESS.2020.3047429","type":"journal-article","article-title":"Segmentation of liver lesions without contrast agents with radiomics-guided densely unet-nested gan","volume":"9","author":"Xiao","year":"2021","journal-title":"Quality Control, Transactions"},{"key":"bpexae524ebib15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1704.07239","type":"journal-article","article-title":"Automatic liver lesion segmentation using a deep convolutional neural network method","author":"Han","year":"2017","journal-title":"arXiv"},{"key":"bpexae524ebib16","doi-asserted-by":"publisher","first-page":"102231","DOI":"10.1016\/j.artmed.2021.102231","type":"journal-article","article-title":"Rms-unet: Residual multi-scale unet for liver and lesion segmentation","volume":"124","author":"Azam Khan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"bpexae524ebib17","doi-asserted-by":"publisher","first-page":"3685","DOI":"10.1007\/s13042-023-01861-6","type":"journal-article","article-title":"Radcu-net: residual attention and dual-supervision cascaded u-net for retinal blood vessel segmentation","volume":"14","author":"Yang","year":"2022","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"bpexae524ebib18","doi-asserted-by":"publisher","first-page":"5976097","DOI":"10.1155\/2021\/5976097","type":"journal-article","article-title":"Serr-u-net: Squeeze-and-excitation residual and recurrent block-based u-net for automatic vessel segmentation in retinal image","volume":"2021","author":"Wang","year":"2021","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"bpexae524ebib19","doi-asserted-by":"publisher","first-page":"174167","DOI":"10.1109\/ACCESS.2019.2940476","type":"journal-article","article-title":"S-unet: a bridge-style u-net framework with a saliency mechanism for retinal vessel segmentation","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"bpexae524ebib20","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.984626","type":"journal-article","article-title":"Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging","volume":"12","author":"Yue","year":"2022","journal-title":"Frontiers in Oncology"},{"key":"bpexae524ebib21","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1038\/s41523-021-00358-x","type":"journal-article","article-title":"Connected-unets: a deep learning architecture for breast mass segmentation","volume":"7","author":"Baccouche","year":"2021","journal-title":"npj Breast Cancer"},{"key":"bpexae524ebib22","doi-asserted-by":"publisher","first-page":"148384","DOI":"10.1109\/ACCESS.2021.3122543","type":"journal-article","article-title":"Mh unet: a multi-scale hierarchical based architecture for medical image segmentation","volume":"9","author":"Ahmad","year":"2021","journal-title":"IEEE Access"},{"key":"bpexae524ebib23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","type":"journal-article","article-title":"Unet++: A nested u-net architecture for medical image segmentation","volume":"11045","author":"Zhou","year":"2018","journal-title":"Lecture Notes in Computer Science"},{"key":"bpexae524ebib24","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/LGRS.2018.2802944","type":"journal-article","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"bpexae524ebib25","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.639930","type":"journal-article","article-title":"Msu-net: Multi-scale u-net for 2d medical image segmentation","volume":"12","author":"Su","journal-title":"Frontiers in Genetics"},{"key":"bpexae524ebib26","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/1471-2105-10-368","type":"journal-article","article-title":"A biosegmentation benchmark for evaluation of bioimage analysis methods","volume":"10","author":"Drelie Gelasca","year":"2009","journal-title":"BMC Bioinform."},{"key":"bpexae524ebib27","doi-asserted-by":"publisher","author":"Drelie Gelasca","year":"2008","DOI":"10.1109\/ICIP.2008.4712145","type":"book"},{"key":"bpexae524ebib28","doi-asserted-by":"publisher","first-page":"13728","DOI":"10.1109\/CVPR46437.2021.01352","type":"journal-article","article-title":"Repvgg: Making vgg-style convnets great again","author":"Ding","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"bpexae524ebib29","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TIP.2024.3354108","type":"journal-article","article-title":"Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention","volume":"33","author":"Chen","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"bpexae524ebib30","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1049\/el:19960621","type":"journal-article","article-title":"Serial concatenation of block and convolutional codes","volume":"32","author":"Benedetto","year":"1996","journal-title":"Electron. Lett."},{"key":"bpexae524ebib31","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/0885-064X(88)90020-9","type":"journal-article","article-title":"On the capabilities of multilayer perceptrons","volume":"4","author":"Baum","year":"1988","journal-title":"J. Complexity"},{"key":"bpexae524ebib32","doi-asserted-by":"publisher","author":"Woo","year":"2018","DOI":"10.1007\/978-3-030-01234-2_1","type":"book"},{"key":"bpexae524ebib33","article-title":"Skipcrossnets: Adaptive skip-cross fusion for road detection","author":"Gong","year":"2023","type":"preprint"},{"key":"bpexae524ebib34","doi-asserted-by":"publisher","first-page":"5393","DOI":"10.30534\/ijatcse\/2020\/175942020","type":"journal-article","article-title":"Binary cross entropy with deep learning technique for image classification","volume":"9","author":"Ruby","year":"2020","journal-title":"International Journal of Advanced Trends in Computer Science and Engineering"},{"key":"bpexae524ebib35","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.asoc.2015.08.060","type":"journal-article","article-title":"Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy","volume":"38","author":"Krawczyk","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"bpexae524ebib36","doi-asserted-by":"publisher","DOI":"10.1109\/ICDSP.2020.9338261","type":"book","article-title":"Rethinking Dice Loss for Medical Image Segmentation","author":"Zhao","year":"2020"},{"key":"bpexae524ebib37","doi-asserted-by":"publisher","first-page":"4207","DOI":"10.1007\/s10462-020-09830-9","type":"journal-article","article-title":"Image segmentation evaluation: a survey of methods","volume":"53","author":"Wang","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"bpexae524ebib38","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1214\/ss\/1177013815","type":"journal-article","article-title":"Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy","volume":"1","author":"Efron","year":"1986","journal-title":"Statistical Science"}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:08:33Z","timestamp":1774505313000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae524e"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,3,25]]},"published-print":{"date-parts":[[2026,4,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/ae524e","relation":{"has-review":[{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/AE524E\/v2\/review1","asserted-by":"object"}]},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]},"assertion":[{"value":"Breast pathology image segmentation based on DESB-net: a fusion strategy of detail enhancement, edge focus, and cross-layer connections","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-25","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}