{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:07:51Z","timestamp":1778828871817,"version":"3.51.4"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110251","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T09:19:29Z","timestamp":1777108769000},"page":"110251","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Quantum generative dynamic graph attention adversarial network for early detection of breast cancer using histopathology images"],"prefix":"10.1016","volume":"122","author":[{"given":"L.R.","family":"Sujithra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Padmanaban","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laxman","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loganathan","family":"Guganathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110251_b0005","article-title":"Breast cancer detection using deep learning: datasets, methods, and challenges ahead","volume":"149","author":"Dar","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110251_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106155","article-title":"MTRRE-Net: a deep learning model for detection of breast cancer from histopathological images","volume":"150","author":"Chattopadhyay","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110251_b0015","article-title":"Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)","volume":"14","author":"Joseph","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110251_b0020","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.csbj.2021.12.028","article-title":"Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning","volume":"20","author":"Yang","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.bspc.2026.110251_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104414","article-title":"SMDetector: small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model","volume":"81","author":"Khan","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"10.1016\/j.bspc.2026.110251_b0030","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.bbe.2022.07.006","article-title":"Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks","volume":"42","author":"Karthik","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110251_b0035","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.icte.2021.11.010","article-title":"The Xception model: a potential feature extractor in breast cancer histology images classification","volume":"8","author":"Sharma","year":"2022","journal-title":"ICT Express"},{"issue":"5","key":"10.1016\/j.bspc.2026.110251_b0040","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.3390\/diagnostics12051152","article-title":"Multi-classification of breast cancer lesions in histopathological images using DEEP_Pachi: multiple self-attention head","volume":"12","author":"Ukwuoma","year":"2022","journal-title":"Diagnostics"},{"issue":"22","key":"10.1016\/j.bspc.2026.110251_b0045","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.3390\/electronics11223767","article-title":"A federated learning framework for breast cancer histopathological image classification","volume":"11","author":"Li","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.bspc.2026.110251_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105569","article-title":"Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies","volume":"146","author":"Liu","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"21","key":"10.1016\/j.bspc.2026.110251_b0055","doi-asserted-by":"crossref","first-page":"4109","DOI":"10.3390\/math10214109","article-title":"BreaST-Net: Multi-class classification of breast cancer from histopathological images using ensemble of swin transformers","volume":"10","author":"Tummala","year":"2022","journal-title":"Mathematics"},{"issue":"5","key":"10.1016\/j.bspc.2026.110251_b0060","doi-asserted-by":"crossref","first-page":"683","DOI":"10.3390\/jpm12050683","article-title":"Multi-class classification of breast cancer using 6b-net with deep feature fusion and selection method","volume":"12","author":"Umer","year":"2022","journal-title":"J. Personal. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110251_b0065","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s11063-021-10555-1","article-title":"A novel lightweight deep learning-based histopathological image classification model for IoMT","volume":"55","author":"Datta Gupta","year":"2023","journal-title":"Neural Process. Lett."},{"issue":"3","key":"10.1016\/j.bspc.2026.110251_b0070","doi-asserted-by":"crossref","first-page":"885","DOI":"10.3390\/cancers15030885","article-title":"Hyperparameter optimizer with deep learning-based decision-support systems for histopathological breast cancer diagnosis","volume":"15","author":"Obayya","year":"2023","journal-title":"Cancers"},{"issue":"3","key":"10.1016\/j.bspc.2026.110251_b0075","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.3390\/ijerph20032131","article-title":"Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network","volume":"20","author":"Das","year":"2023","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"6","key":"10.1016\/j.bspc.2026.110251_b0080","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.3390\/math11061429","article-title":"Multi-method diagnosis of histopathological images for early detection of breast cancer based on hybrid and deep learning","volume":"11","author":"Al-Jabbar","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.bspc.2026.110251_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105152","article-title":"Breast cancer diagnosis from histopathology images using deep neural network and XGBoost","volume":"86","author":"Maleki","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"10.1016\/j.bspc.2026.110251_b0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2023RS007761","article-title":"Breast cancer detection based on simplified deep learning technique with histopathological image using BreaKHis database","volume":"58","author":"Toma","year":"2023","journal-title":"Radio Sci."},{"issue":"10","key":"10.1016\/j.bspc.2026.110251_b0095","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","article-title":"A deep learning method for breast cancer classification in the pathology images","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110251_b0100","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3419004","article-title":"ADBNet: an attention-guided deep broad convolutional neural network for the classification of breast cancer histopathology images","author":"Rahman","year":"2024","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.bspc.2026.110251_b0105","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3390\/jimaging10050108","article-title":"Optimizing vision transformers for histopathology: pretraining and normalization in breast cancer classification","volume":"10","author":"Baroni","year":"2024","journal-title":"J. Imag."},{"key":"10.1016\/j.bspc.2026.110251_b0110","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3397667","article-title":"SwinCNN: an integrated swin trasformer and CNN for improved breast cancer grade classification","author":"Sreelekshmi","year":"2024","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.bspc.2026.110251_b0115","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.3390\/cancers16122222","article-title":"Ensemble deep learning-based image classification for breast cancer subtype and invasiveness diagnosis from whole slide image histopathology","volume":"16","author":"Balasubramanian","year":"2024","journal-title":"Cancers"},{"issue":"16","key":"10.1016\/j.bspc.2026.110251_b0120","doi-asserted-by":"crossref","first-page":"9311","DOI":"10.1007\/s00521-025-10984-2","article-title":"Deep learning and vision transformers-based framework for breast cancer and subtype identification","volume":"37","author":"Jahan","year":"2025","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.bspc.2026.110251_b0125","first-page":"1","article-title":"QCNN-Swin-UNet: quantum convolutional neural network integrated with optimized swin-UNet for efficient liver tumor segmentation and classification on edge devices","author":"Idress","year":"2025","journal-title":"J. Imag. Inform. Med."},{"issue":"5","key":"10.1016\/j.bspc.2026.110251_b0130","doi-asserted-by":"crossref","first-page":"653","DOI":"10.3390\/diagnostics16050653","article-title":"Ensemble deep learning-based high-precision framework for breast cancer detection from histopathological images","volume":"16","author":"Ahmad","year":"2026","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.bspc.2026.110251_b0135","doi-asserted-by":"crossref","DOI":"10.1155\/ijbc\/5948413","article-title":"Automated segmentation and analysis of histopathological breast cancer images for enhanced IDC diagnosis and assessment using MobileNetV2+ U\u2010Net with label propagation","volume":"2026","author":"Inamdar","year":"2026","journal-title":"Int. J. Breast Cancer"},{"issue":"2","key":"10.1016\/j.bspc.2026.110251_b0140","article-title":"A hybrid SwinTConvNeXt with learnable dynamic gating fusion for breast cancer histopathology image classification","volume":"8","author":"Makina","year":"2026","journal-title":"Eng. Rep."},{"issue":"03","key":"10.1016\/j.bspc.2026.110251_b0145","article-title":"China commodity price index (CCPI) forecasting via the neural network","volume":"12","author":"Jin","year":"2025","journal-title":"Int. J. Finan. Eng."},{"issue":"4","key":"10.1016\/j.bspc.2026.110251_b0150","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1002\/ajae.12041","article-title":"Corn cash price forecasting","volume":"102","author":"Xu","year":"2020","journal-title":"Am. J. Agric. Econ."},{"issue":"1","key":"10.1016\/j.bspc.2026.110251_b0155","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s13563-024-00472-9","article-title":"Forecasts of coking coal futures price indices through Gaussian process regressions: B Jin and X. Xu","volume":"38","author":"Jin","year":"2025","journal-title":"Min. Econom."},{"key":"10.1016\/j.bspc.2026.110251_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106870","article-title":"Price forecasts of ten steel products using Gaussian process regressions","volume":"126","author":"Xu","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.bspc.2026.110251_b0165","doi-asserted-by":"crossref","DOI":"10.1142\/S308284142550008X","article-title":"A study of contemporaneous residential real estate price causation across major jiangsu province cities: Methodology using vector error-correction models and directed acyclic graphs","author":"Jin","year":"2025","journal-title":"Econom. Open"},{"key":"10.1016\/j.bspc.2026.110251_b0170","article-title":"An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities","volume":"7","author":"Xu","year":"2023","journal-title":"Decis. Anal. J."},{"key":"10.1016\/j.bspc.2026.110251_b0175","article-title":"Individual time series and composite forecasting of the chinese stock index","volume":"5","author":"Xu","year":"2021","journal-title":"Mach. Learn. Appl."},{"issue":"47","key":"10.1016\/j.bspc.2026.110251_b0180","doi-asserted-by":"crossref","first-page":"20544","DOI":"10.1039\/D0NJ03868G","article-title":"Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids","volume":"44","author":"Zhang","year":"2020","journal-title":"New J. Chem."},{"key":"10.1016\/j.bspc.2026.110251_b0185","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.powtec.2021.04.072","article-title":"Solid particle erosion rate predictions through LSBoost","volume":"388","author":"Zhang","year":"2021","journal-title":"Powder Technol."},{"issue":"34","key":"10.1016\/j.bspc.2026.110251_b0190","doi-asserted-by":"crossref","first-page":"15255","DOI":"10.1039\/D1NJ01523K","article-title":"Modeling and prediction of lattice parameters of binary spinel compounds (AM 2 X 4) using support vector regression with Bayesian optimization","volume":"45","author":"Alade","year":"2021","journal-title":"New J. Chem."},{"key":"10.1016\/j.bspc.2026.110251_b0195","article-title":"Disordered MgB2 superconductor critical temperature modeling through regression trees","volume":"597","author":"Zhang","year":"2022","journal-title":"Physica C (Amsterdam, Neth.)"},{"issue":"5","key":"10.1016\/j.bspc.2026.110251_b0200","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s43674-022-00045-9","article-title":"Canola and soybean oil price forecasts via neural networks","volume":"2","author":"Xu","year":"2022","journal-title":"Adv. Comput. Intell."},{"issue":"2","key":"10.1016\/j.bspc.2026.110251_b0205","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s11408-022-00421-y","article-title":"Neural network predictions of the high-frequency CSI300 first distant futures trading volume","volume":"37","author":"Xu","year":"2023","journal-title":"Fin. Markets. Portfolio Mgmt."},{"issue":"8","key":"10.1016\/j.bspc.2026.110251_b0210","doi-asserted-by":"crossref","first-page":"4097","DOI":"10.1007\/s11760-023-02641-9","article-title":"Gradient domain weighted guided image filtering","volume":"17","author":"Wang","year":"2023","journal-title":"SIViP"},{"key":"10.1016\/j.bspc.2026.110251_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117053","article-title":"GCNFusion: an efficient graph convolutional network based model for information diffusion","volume":"202","author":"Fatemi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110251_b0220","article-title":"Dynamic graph attention networks for point cloud landslide segmentation","volume":"124","author":"Wei","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.bspc.2026.110251_b0225","series-title":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","first-page":"1810","article-title":"A quantum generative adversarial network-based intrusion detection system","author":"Rahman","year":"2023"},{"key":"10.1016\/j.bspc.2026.110251_b0230","doi-asserted-by":"crossref","first-page":"30796","DOI":"10.1109\/ACCESS.2024.3367440","article-title":"An opposition-based great wall construction metaheuristic algorithm with gaussian mutation for feature selection","volume":"12","author":"Zitouni","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.110251_b0235","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/diagnostics14010095","article-title":"Histopathological image diagnosis for breast cancer diagnosis based on deep mutual learning","volume":"14","author":"Kaur","year":"2023","journal-title":"Diagnostics"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008050?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008050?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:12:58Z","timestamp":1778825578000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426008050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":47,"alternative-id":["S1746809426008050"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110251","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quantum generative dynamic graph attention adversarial network for early detection of breast cancer using histopathology images","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110251","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110251"}}