Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F: Cancer statistics for the year 2020: An overview. Int J Cancer 149:778–789, 2021.

Article  CAS  Google Scholar 

Siegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2021. CA Cancer J Clin 71:7–33, 2021.

PubMed  Google Scholar 

Gezici S: Novel approaches in cancer treatment: Tumor targeted therapy. In: Handbook of Cancer and Immunology. Cham: Springer International Publishing, 2025, pp 1–34.

Orrantia-Borunda E, Anchondo-Nuñez P, Acuña-Aguilar LE, Gómez-Valles FO, Ramírez-Valdespino CA: Subtypes of breast cancer. Breast Cancer, 2022.

Wignarajah S, Chianella I, Tothill IE: Development of electrochemical immunosensors for HER-1 and HER-2 analysis in serum for breast cancer patients. Biosensors 13:355, 2023.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Elboga U, Sahin E, Kus T, Cayirli YB, Aktas G, Uzun E, Celen YZ: Superiority of 68Ga-FAPI PET/CT scan in detecting additional lesions compared to 18FDG PET/CT scan in breast cancer. Ann Nucl Med 35:1321–1331, 2021.

Article  PubMed  CAS  Google Scholar 

Malhotra GK, Zhao X, Band H, Band V: Histological, molecular and functional subtypes of breast cancers. Cancer Biol Ther 10:955–960, 2010.

Article  PubMed  PubMed Central  Google Scholar 

Warren Andersen S, Newcomb PA, Hampton JM, Titus-Ernstoff L, Egan KM, Trentham-Dietz A: Reproductive factors and histologic subtype in relation to mortality after a breast cancer diagnosis. Breast Cancer Res Treat 130:975–980, 2011.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Veta M, Pluim JPW, van Diest PJ, Viergever MA: Breast cancer histopathology image analysis: A review. IEEE Trans Biomed Eng 61:1400–1411, 2014.

Article  PubMed  Google Scholar 

Johnson KS, Conant EF, Soo MS: Molecular subtypes of breast cancer: A review for breast radiologists. J Breast Imaging 3:12–24, 2021.

Article  PubMed  Google Scholar 

Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R: Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67, 2018.

Article  PubMed  Google Scholar 

Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W: Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9:12495, 2019.

Article  PubMed  PubMed Central  Google Scholar 

Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R: A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292:60–66, 2019.

Article  PubMed  Google Scholar 

Acikgoz H, Korkmaz D, Talan T: An automated diagnosis of Parkinson’s disease from MRI scans based on enhanced residual dense network with attention mechanism. J Digit Imaging 38:1935–1949, 2025.

Article  Google Scholar 

Chakravarthy SS, Bharanidharan N, Rajaguru H: Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer. IRBM 44:100749, 2023.

Article  Google Scholar 

Elkorany AS, Elsharkawy ZF: Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance. Sci Rep 13:2663, 2023.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Hekal AA, Moustafa HED, Elnakib A: Ensemble deep learning system for early breast cancer detection. Evol Intell 16:1045–1054, 2023.

Article  Google Scholar 

Chakravarthy SRS, Bharanidharan N, Rajaguru H: Multi-deep CNN based experimentations for early diagnosis of breast cancer. IETE J Res 69:7326–7341, 2023.

Article  Google Scholar 

Abunasser BS, Al-Hiealy MRJ, Zaqout IS, Abu-Naser SS: Convolution neural network for breast cancer detection and classification using deep learning. Asian Pac J Cancer Prev 24:531–538, 2023.

Article  PubMed  PubMed Central  Google Scholar 

Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J: Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobioscience 17:237–242, 2018.

Article  PubMed  Google Scholar 

Guo YJ, Yin R, Zhang Q, Han JQ, Dou ZX, Wang PB, Ma WJ: MRI-based kinetic heterogeneity evaluation in the accurate assessment of axillary lymph node status in breast cancer using a hybrid CNN-RNN model. J Magn Reson Imaging, 2023.

Hayum AA, Jaya J, Sivakumar R, et al: An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network. Sci Rep 14:6290, 2024.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Zebari DA, Zeebaree DQ, Abdulazeez AM, Haron H, Hamed HNA: Improved threshold-based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access 8:203097–203116, 2020.

Article  Google Scholar 

Khanna P, Sahu M, Singh BK, Bhateja V: Early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer MRI images using combined pre-trained convolutional neural network and machine learning. Measurement 207:112269, 2023.

Article  Google Scholar 

Hasan AM, Al-Waely NK, Aljobouri HK, Jalab HA, Ibrahim RW, Meziane F: Molecular subtypes classification of breast cancer in DCE-MRI using deep features. Expert Syst Appl 236:121371, 2024.

Article  Google Scholar 

Aslan MF: A hybrid end-to-end learning approach for breast cancer diagnosis: Convolutional recurrent network. Comput Electr Eng 105:108562, 2023.

Article  Google Scholar 

Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC: A novel deep learning-based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1–6, 2019.

Article  Google Scholar 

Wang Z, Li M, Wang H, Jiang H, Yao Y, Zhang H, Xin J: Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7:105146–105158, 2019.

Article  Google Scholar 

Fridman N, Avraham S, Erel O, Zehavi T, Basri R, Shamir R, Amit G: BreastDCEDL: Curating a comprehensive DCE-MRI dataset and developing a transformer implementation for breast cancer treatment response prediction. arXiv preprint arXiv:2506.12190, 2025.

Moslemi A, Fatemizadeh E, Niknami M, Shekoohi-Shooli F, Hadizadeh Esfahani A, Hosseini SA, Ay MR: A priori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging. Front Oncol 14:1359148, 2024.

Article  PubMed  PubMed Central  Google Scholar 

Tummala S, Kim J, Kadry S: BreaST-Net: Multi-class classification of breast cancer from histopathological images using ensemble of Swin Transformers. Mathematics 10:4109, 2022.

Article  Google Scholar 

Ahmed MR, Rahman H, Limon ZH, Siddiqui MIH, Khan MA, Pranta ASUK, Haque R, Swapno SMMR, Cho YI, Abdallah MS: Hierarchical Swin transformer ensemble with explainable AI for robust and decentralized breast cancer diagnosis. Bioengineering 12:651, 2025.

Article  PubMed  PubMed Central  Google Scholar 

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y: Generative adversarial nets. Adv Neural Inf Process Syst 27:2672–2680, 2014.

Google Scholar 

Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A: Improved training of Wasserstein GANs. Adv Neural Inf Process Syst, 2017.

Yu W, Si C, Zhou P, Luo M, Zhou Y, Feng J, Yan S, Wang X: MetaFormer baselines for vision. IEEE Trans Pattern Anal Mach Intell 46:896–912, 2024.

Article  Google Scholar 

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I: Attention is all you need. Adv Neural Inf Process Syst 30:5998–6008, 2017.

Google Scholar 

Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y: Transformer in transformer. Proc IEEE Conf Comput Vis Pattern Recognit:15908–15919, 2022.

Dosovitskiy A, Beyer L, Kolesnikov A, et al: An image is worth 16×16 words: Transformers for image recognition at scale. Int Conf Learn Represent, 2021.

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B: Swin transformer: Hierarchical vision transformer using shifted windows. Proc IEEE Int Conf Comput Vis:10012–10022, 2021.

Wu H, Xiao B, Codella N, Liu M, Dai X, Yuan L, Zhang L: CvT: Introducing convolutions to vision transformers. Proc IEEE Int Conf Comput Vis:22–31, 2021.

Choromanski K, Likhosherstov V, Dohan D, et al: Rethinking attention with performers. Int Conf Learn Represent, 2021.

He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit:770–778, 2016.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC: MobileNetV2: Inverted residuals and linear bottlenecks. Proc IEEE Conf Comput Vis Pattern Recognit:4510–4520, 2018.

Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105, 2012.

Google Scholar 

Tan M, Le Q: EfficientNet: Rethinking model scaling for convolutional neural networks. Proc Int Conf Mach Learn:6105–6114, 2019.

Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. Int Conf Learn Represent, 2015.

Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H: Training data-efficient image transformers and distillation through attention. Proc Int Conf Mach Learn:10347–10357, 2021.

Graham B, El-Nouby A, Touvron H, et al: LeViT: A vision transformer in ConvNet’s clothing for faster inference. Proc IEEE Int Conf Comput Vis, 2021.

Li J, Xia X, Li W, et al: Next-ViT: Next generation vision transformer for efficient deployment in realistic industrial scenarios. arXiv preprint arXiv:2207.05501, 2022.

Yang J, Li C, Dai X, Yuan L, Gao J: Focal modulation networks. Adv Neural Inf Process Syst, 2022.

Dai Z, Liu H, Le QV, Tu L, Han J: CoAtNet: Marrying convolution and attention for all data sizes. arXiv preprint arXiv:2106.04803, 2021.

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B: Swin transformer: Hierarchical vision transformer using shifted windows. Proc IEEE Int Conf Comput Vis, 2021.

Woo S, Debnath S, Hu R, Chen X, Liu Z, So KI, Xie S: ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. Proc IEEE Conf Comput Vis Pattern Recognit, 2023.