Siegel R L, Miller K D, Wagle N S, et al. Cancer statistics, 2023[J]. CA: a cancer journal for clinicians, 2023, 73(1): 17–48.
Johnson R H, Anders C K, Litton J K, et al. Breast cancer in adolescents and young adults[J]. Pediatric blood & cancer, 2018, 65(12): e27397.
Dharani Devi G, V R, Jeyalakshmi J. Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach[J]. Journal of Imaging Informatics in Medicine, 2024: 1–17.
Chen G, Li L, Dai Y, et al. AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images[J]. IEEE Transactions on Medical Imaging, 2022, 42(5): 1289-1300.
Wells P N T, Liang H D. Medical ultrasound: imaging of soft tissue strain and elasticity[J]. Journal of the Royal Society Interface, 2011, 8(64): 1521-1549.
Article PubMed PubMed Central Google Scholar
Huang D Y, Yusuf G T, Daneshi M, et al. Contrast-enhanced ultrasound (CEUS) in abdominal intervention[J]. Abdominal Radiology, 2018, 43: 960-976.
Siqueira L G B, Areas V S, Ghetti A M, et al. Color Doppler flow imaging for the early detection of nonpregnant cattle at 20 days after timed artificial insemination[J]. Journal of Dairy Science, 2013, 96(10): 6461-6472.
Article CAS PubMed Google Scholar
Jiang M, Li C L, Chen R X, et al. Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists[J]. European Journal of Radiology, 2021, 141: 109781.
Azam S, Montaha S, Raiaan M A K, et al. An automated decision support system to analyze malignancy patterns of breast masses employing medically relevant features of ultrasound images[J]. Journal of Imaging Informatics in Medicine, 2024, 37(1): 45-59.
Article PubMed PubMed Central Google Scholar
Huang R, Lin M, Dou H, et al. Boundary-rendering network for breast lesion segmentation in ultrasound images[J]. Medical image analysis, 2022, 80: 102478.
Veluchamy S, Sudharson S, Annamalai R, et al. Automated Detection of COVID-19 from Multi-modal Imaging Data Using Optimized Convolutional Neural Network Model[J]. Journal of Imaging Informatics in Medicine, 2024: 1–15.
Dai L, Fang R, Li H, et al. Clinical report guided retinal microaneurysm detection with multi-sieving deep learning[J]. IEEE transactions on medical imaging, 2018, 37(5): 1149-1161.
Abdekhoda M, Ahmadi M, Dehnad A, et al. Applying electronic medical records in health care[J]. Applied clinical informatics, 2016, 7(02): 341-354.
Article PubMed PubMed Central Google Scholar
Du C, He H, Jin Y. Contrast with major classifier vectors for federated medical relation extraction with heterogeneous label distribution[J]. Applied Intelligence, 2023, 53(23): 28895-28909.
Savova G K, Danciu I, Alamudun F, et al. Use of natural language processing to extract clinical cancer phenotypes from electronic medical records[J]. Cancer research, 2019, 79(21): 5463-5470.
Article CAS PubMed PubMed Central Google Scholar
Berg W A, Campassi C, Langenberg P, et al. Breast Imaging Reporting and Data System: inter-and intraobserver variability in feature analysis and final assessment[J]. American Journal of Roentgenology, 2000, 174(6): 1769-1777.
Article CAS PubMed Google Scholar
Carrilero-Mardones M, Parras-Jurado M, Nogales A, et al. Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms[J]. Journal of Imaging Informatics in Medicine, 2024: 1–15.
Pötsch N, Vatteroni G, Clauser P, et al. Using the Kaiser Score as a clinical decision rule for breast lesion classification: Does computer-assisted curve type analysis improve diagnosis?[J]. European Journal of Radiology, 2024, 170: 111271.
Chen G, Li L, Zhang J, et al. Rethinking the unpretentious U-net for medical ultrasound image segmentation[J]. Pattern Recognition, 2023, 142: 109728.
Chen G, Dai Y, Zhang J. C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation[J]. Computer Methods and Programs in Biomedicine, 2022, 225: 107086.
Ting F F, Tan Y J, Sim K S. Convolutional neural network improvement for breast cancer classification[J]. Expert Systems with Applications, 2019, 120: 103-115.
Yang, Z., Ran, L., Zhang, S., Xia, Y., & Zhang, Y. (2019). EMS-Net: Ensemble of multiscale convolutional neural networks for classification of breast cancer histology images. Neurocomputing, 366, 46–53. https://doi.org/10.1016/J.NEUCOM.2019.07.080.
Zhuang Z, Yang Z, Raj A N J, et al. Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion[J]. Computer methods and programs in biomedicine, 2021, 208: 106221.
Hejduk P, Marcon M, Unkelbach J, et al. Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network[J]. European radiology, 2022, 32(7): 4868-4878.
Article PubMed PubMed Central Google Scholar
Tsai K J, Chou M C, Li H M, et al. A high-performance deep neural network model for BI-RADS classification of screening mammography[J]. Sensors, 2022, 22(3): 1160.
Article PubMed PubMed Central Google Scholar
Pi Y, Li Q, Qi X, et al. Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function[J]. Applied Intelligence, 2022, 52(11): 12943-12956.
Gong X, Zhao X, Fan L, et al. BUS-net: a bimodal ultrasound network for breast cancer diagnosis[J]. International Journal of Machine Learning and Cybernetics, 2022, 13(11): 3311-3328.
Qiao M, Liu C, Li Z, et al. Breast tumor classification based on MRI-US images by disentangling modality features[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(7): 3059-3067.
Yang X, Xi X, Wang K, et al. Triple-attention interaction network for breast tumor classification based on multi-modality images[J]. Pattern Recognition, 2023, 139: 109526.
Yan R, Zhang F, Rao X, et al. Richer fusion network for breast cancer classification based on multi-modal data[J]. BMC Medical Informatics and Decision Making, 2021, 21: 1-15.
Shi E, Gong X, Luo J, et al. HTBN: A Heterogeneous Network for Breast Ultrasound Image Classification[C]//2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2019: 987–993.
Wang H, Hou J, Chen H. Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis[J]. arXiv preprint arXiv:2410.15446, 2024.
Chen B, Zhang Z, Li Y, et al. Multi-label chest X-ray image classification via semantic similarity graph embedding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(4): 2455-2468.
Chougrad H, Zouaki H, Alheyane O. Multi-label transfer learning for the early diagnosis of breast cancer[J]. Neurocomputing, 2020, 392: 168-180.
El-Fiky A, Shouman M A, Hamada S, et al. Multi-label transfer learning for identifying lung diseases using chest X-rays[C]//2021 International Conference on Electronic Engineering (ICEEM). IEEE, 2021: 1–6.
Gour N, Khanna P. Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network[J]. Biomedical signal processing and control, 2021, 66: 102329.
He J, Li C, Ye J, et al. Multi-label ocular disease classification with a dense correlation deep neural network[J]. Biomedical Signal Processing and Control, 2021, 63: 102167.
Lin L, Wang G, Zuo W, et al. Cross-domain visual matching via generalized similarity measure and feature learning[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1089-1102.
Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251–1258.
Xiao T, Liu Y, Huang Y, et al. Enhancing multiscale representations with transformer for remote sensing image semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-16.
Mehta S, Rastegari M. Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv 2021[J]. arXiv preprint arXiv:2110.02178.
Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92.
Rognin N G, Arditi M, Mercier L, et al. Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound[J]. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2010, 57(11): 2503-2511.
Al-Dhabyani W, Gomaa M, Khaled H, et al. Dataset of breast ultrasound images[J]. Data in brief, 2020, 28: 104863.
Pawłowska A, Ćwierz-Pieńkowska A, Domalik A, et al. Curated benchmark dataset for ultrasound based breast lesion analysis[J]. Scientific Data, 2024, 11(1): 148.
留言 (0)