ESE and Transfer Learning for Breast Tumor Classification

Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Journal of Computing, 201, 71(3). https://doi.org/10.3322/caac.21660.

Kabir, Shahriar Mahmud, and Mohammed Imamul Hassan Bhuiyan. CWC-MP-MC Image-based breast tumor classification using an optimized Vision Transformer (ViT). Biomedical Signal Processing and Control 100 (2025): 106941.

Zhang Huina, and Yan Peng. Unique Molecular Alteration of Lobular Breast Cancer: Association with Pathological Classification, Tumor Biology and Behavior, and Clinical Management. Cancers, 17.3 (2025): 417.

Google Scholar 

Gong Li-Hua, et al. Quantum k-nearest neighbor classification algorithm via a divide-and-conquer strategy. Advanced Quantum Technologies, 7.6 (2024): 2300221.

Google Scholar 

Zhu J Y, He H L, Jiang X C, et al. Multimodal ultrasound features of breast cancers: correlation with molecular subtypes[J]. BMC Medical Imaging, 2023, 23(1): 57.

PubMed  PubMed Central  Google Scholar 

Samardzija A, Selvaganesan K, Zhang H Z, et al. Low-field, low-cost, point-of-care magnetic resonance imaging[J]. Annual Review of Biomedical Engineering, 2024, 26.

Hu Z, Wang J, Zhang C, et al. Uncertainty modeling for multicenter autism spectrum disorder classification using Takagi–Sugeno–Kang fuzzy systems[J]. IEEE Transactions on Cognitive and Developmental Systems, 2021, 14(2): 730-739.

Google Scholar 

Ma W J, Zhao Y M, Ji Y, Gou XP, Jian XQ, et al. Breast cancer molecular subtype prediction by mammographic radiomic features. Academic Radiology, 2019, 26 (2) : 196-201. https://doi.org/10.1016/j.acra.2018.01.023.

Article  PubMed  Google Scholar 

Li Y, Cui W G, Huang H, et al. Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach[J]. Knowledge-Based Systems, 2019, 164: 96-106.

Google Scholar 

Liu S, Wu F Y. Self-training dictionary based approximated ℓ0 norm constraint reconstruction for compressed ECG[J]. Biomedical Signal Processing and Control, 2021, 68: 102768.

Google Scholar 

Ni, YY., Wu, FY. & Yang, HZ. An Automatic Threshold OMP Algorithm Based on QR Decomposition for Magnetic Resonance Image Reconstruction. Circuits Syst Signal Process 43, 3697–3717 (2024). https://doi.org/10.1007/s00034-024-02624-2

Zhang L, Su G, Yin J, et al. Bioinspired scene classification by deep active learning with remote sensing applications[J]. IEEE Transactions on Cybernetics, 2021, 52(7): 5682-5694.

Google Scholar 

Silver David, Schrittwieser Julian, Simonyan Karen, et al. Mastering the game of Go without human knowledge. Nature, 550(7676): 354–358. https://doi.org/10.1038/nature24270.

Kang J, Gwak J. Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification[J]. Multimedia Tools and Applications, 2022, 81(16): 22355-22377.

Google Scholar 

Zhou Nan-Run, et al. Quantum K-nearest-neighbor image classification algorithm based on KL transform. International Journal of Theoretical Physics, 60 (2021): 1209-1224.

Google Scholar 

Furtney I, Bradley R, Kabuka M R. Patient graph deep learning to predict breast cancer molecular subtype[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2023, 20(5): 3117-3127.

PubMed  PubMed Central  Google Scholar 

Chen M, Kong C, Lin G, et al. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study[J]. EClinicalMedicine, 2023, 63.

Sun R, et al. Prediction of breast cancer molecular subtypes using DCE-MRI based on CNNs combined with ensemble learning. Physics in Medicine and Biology, 2021, 66: 175009. https://doi.org/10.1088/1361-6560/ac195a.

Article  Google Scholar 

Pang Meng, et al. Heterogeneous prototype learning from contaminated faces across domains via disentangling latent factors. IEEE Transactions on Neural Networks and Learning Systems, 36.4 (2025): 7169-7183.

Google Scholar 

Hassan N M, Hamad S, Mahar K. Mammogram breast cancer CAD systems for mass detection and classification: a review[J]. Multimedia Tools and Applications, 2022, 81(14): 20043-20075.

Google Scholar 

Bloice MD, Roth PM, Holzinger A. Biomedical image augmentation using augmentor. Bioinformatics. (2019) 35:4522-4524. https://doi.org/10.1093/bioinformatics/btz259.

Article  CAS  PubMed  Google Scholar 

Xiao M X, Li Y, Yan X, et al. Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example[C]//Proceedings of the 2024 7th International Conference on Machine Vision and Applications. 2024: 145–149.

Ali M D, Saleem A, Elahi H, et al. Breast cancer classification through meta-learning ensemble technique using convolution neural networks[J]. Diagnostics, 2023, 13(13): 2242.

PubMed  PubMed Central  Google Scholar 

Jie, Shen, et al. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. https://doi.org/10.1109/TPAMI.2019.2913372.

Howard A , et al, Searching for MobileNetV3, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314-1324.

Google Scholar 

Li Y, Song P. Research progress of Transfer learning in medical image classification. Journal of Image and Graphics, 202, 27(03): 672–686.

M. E. H. Chowdhury et al., Can AI Help in Screening Viral and COVID-19 Pneumonia?. IEEE Access, 2020, 8: 132665–132676. https://doi.org/10.34740/kaggle/dsv/3122958.

Singh S, Srikanth V, Kumar S, et al. IOT Based Deep Learning framework to Diagnose Breast Cancer over Pathological Clinical Data[C]//2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, 2022, 2: 731–735.

Shafiq M, Gu Z. Deep residual learning for image recognition: A survey[J]. Applied sciences, 2022, 12(18): 8972.

CAS  Google Scholar 

Singh D, Kumar V, Kaur M. Densely connected convolutional networks-based COVID-19 screening model[J]. Applied Intelligence, 2021, 51: 3044-3051.

PubMed  Google Scholar 

Taye M M. Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions[J]. Computation, 2023, 11(3): 52.

Google Scholar 

Comments (0)

No login
gif