Zardavas D, Irrthum A, Swanton C, Piccart M. Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol. 2015;12:381–94.
Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ. Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol. 2011;22:1736–47.
CAS PubMed PubMed Central Google Scholar
Zhou BY, Wang LF, Yin HH, Wu TF, Ren TT, Peng C, et al. Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: a prospective and multicentre study. eBioMedicine. 2021;74:103684.
CAS PubMed PubMed Central Google Scholar
Yeo SK, Guan JL. Breast cancer: multiple subtypes within a tumor?. Trends Cancer. 2017;3:753–60.
CAS PubMed PubMed Central Google Scholar
Wang M, He X, Chang Y, Sun G, Thabane L. A sensitivity and specificity comparison of fine needle aspiration cytology and core needle biopsy in evaluation of suspicious breast lesions: a systematic review and meta-analysis. Breast. 2017;31:157–66.
Zhu JY, He HL, Jiang XC, Bao HW, Chen F. Multimodal ultrasound features of breast cancers: correlation with molecular subtypes. BMC Med Imaging. 2023;23:57.
PubMed PubMed Central Google Scholar
Zhang L, Li J, Xiao Y, Cui H, Du G, Wang Y, et al. Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision. Sci Rep. 2015;5:11085.
CAS PubMed PubMed Central Google Scholar
Yang S, Gao X, Liu L, Shu R, Yan J, Zhang G, et al. Performance and reading time of automated breast US with or without computer-aided detection. Radiology. 2019;292:540–9.
Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. Lancet Oncol. 2019;20:193–201.
Trepanier C, Huang A, Liu M, Ha R. Emerging uses of artificial intelligence in breast and axillary ultrasound. Clin Imaging. 2023;100:64–68.
Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine Learning in oncology: a clinical appraisal. Cancer Lett. 2020;481:55–62.
Jiang M, Zhang D, Tang SC, Luo XM, Chuan ZR, Lv WZ, et al. Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. Eur Radiol. 2021;31:3673–82.
Zhang X, Li H, Wang C, Cheng W, Zhu Y, Li D, et al. Evaluating the accuracy of breast cancer and molecular subtype diagnosis by ultrasound image deep learning model. Front Oncol. 2021;11:623506.
CAS PubMed PubMed Central Google Scholar
Zhao G, Kong D, Xu X, Hu S, Li Z, Tian J. Deep learning-based classification of breast lesions using dynamic ultrasound video. Eur J Radiol. 2023;165:110885.
Qiu S, Zhuang S, Li B, Wang J, Zhuang Z. Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics. Front Oncol. 2023;13:1274557.
PubMed PubMed Central Google Scholar
Meng H, Lin Z, Yang F, Xu Y, Cui L. Knowledge distillation in medical data mining: a survey. 5th international conference on crowd science and engineering. ACM Digital Library. 2021:175–82.
Xie X, Niu J, Liu X, Chen Z, Tang S, Yu S. A survey on incorporating domain knowledge into deep learning for medical image analysis. Med Image Anal. 2021;69:101985.
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;8:679–98.
Kataoka H, Wakamiya T, Hara K, Satoh Y. Would mega-scale datasets further enhance spatiotemporal 3D CNNs? arXiv: 2004.04968 [Preprint] 2020. Available from https://arxiv.org/abs/2004.04968.
Park W, Kim D, Lu Y, Cho M. Relational knowledge distillation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. 2019: 3962–71.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE. 2017:618–26.
Tian Y, Krishnan D, Isola P. Contrastive representation distillation. arXiv: 1910.10699 [Preprint] 2019. Available from: https://arxiv.org/abs/1910.10699.
Passalis N, Tefas A. Learning deep representations with probabilistic knowledge transfer. European Conference on Computer Vision. Cham: Springer International Publishing. 2018:283–99.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. 2016: 770–8.
Luo WQ, Huang QX, Huang XW, Hu HT, Zeng FQ, Wang W. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS. Sci Rep. 2019;9:11921.
PubMed PubMed Central Google Scholar
Zhang Y, Chen JH, Lin Y, Chan S, Zhou J, Chow D, et al. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers. Eur Radiol. 2021;31:2559–67.
Mao N, Zhang H, Dai Y, Li Q, Lin F, Gao J, et al. Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study. Br J Cancer. 2023;128:793–804.
de Margerie-Mellon C, Chassagnon G. Artificial intelligence: a critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging. 2023;104:11–17.
Raja H, Akram MU, Shaukat A, Khan SA, Alghamdi N, Khawaja SG, et al. Extraction of retinal layers through convolution neural network (CNN) in an OCT image for glaucoma diagnosis. J Digital Imaging. 2020;33:1428–42.
Musthafa MM, RM T, VK V, Guluwadi S. Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification. BMC Med Imaging. 2024;24:201.
PubMed PubMed Central Google Scholar
Xu C, Coen-Pirani P, Jiang X. Empirical study of overfitting in deep learning for predicting breast cancer metastasis. Cancers. 2023;15:1969.
PubMed PubMed Central Google Scholar
Zhang T, Tan T, Han L, Appelman L, Veltman J, Wessels R, et al. Predicting breast cancer types on and beyond molecular level in a multi-modal fashion. NPJ Breast Cancer. 2023;9:16.
CAS PubMed PubMed Central Google Scholar
Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med. 2019;179:293–4.
Tan PH, Ellis I, Allison K, Brogi E, Fox SB, Lakhani S, et al. The 2019 World Health Organization classification of tumours of the breast. Histopathology. 2020;77:181–5.
Pölcher M, Braun M, Tischitz M, Hamann M, Szeterlak N, Kriegmair A, et al. Concordance of the molecular subtype classification between core needle biopsy and surgical specimen in primary breast cancer. Arch Gynecol Obstet. 2021;304:783–90.
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