Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

By Benjamin Q. Huynh, Hui Li, Maryellen Lissak Giger. Journal of Medical Imaging, 2016

486 citations recorded by the source corpus.

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Deep learning in medical imaging and radiation therapy. (2018, methodology): “196,197 Figure 4 shows one possible method for combining CNN-extracted and conventional radiomics features.”
Deep learning for image classification on very small datasets using transfer learning (2019, background): “Transfer learning with deep neural network has been explored in medical image classification in [24] and [25] in a manner related to my work.”
A Decade Survey of Transfer Learning (2010–2020) (2020, background): “To address this issue, inductive TL [14], [88], [101]–[103] has started receiving more and more attention.”
State-of-the-art review on deep learning in medical imaging. (2019, methodology): “The methodology focuses on classification of breast lesions from mammographic images using transfer learning via CNN framework (50).”
A survey on deep learning in medical image analysis (2017, methodology): “Huynh et al. (2016) MG CNN Pre-trained CNN on natural image patches applied to mass classification”
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks (2017, background): “Others have focused on training a deep neural network for classifying a small region of interest into one of a few categories, assuming an existing mass detection system (9, 30–32).”