Figure 1.

Transfer learning (TL) methods. There are two types of transfer learning used for breast cancer diagnosis via ultrasound imaging, depending on the source of pre-training data: cross-domain (model pre-trained on natural images is used) and cross-modal (model pre-trained on medical images is used). These two transfer learning approaches are feature extractors (convolution layers are used as a frozen feature extractor to match with a new task such as breast cancer classification) and fine-tuning (where instead of freezing convolution layers of the well-trained convolutional neural network (CNN) model, their weights are updated during the training process). X, input; Y, output; NI, natural image; MRI, magnetic resonance imaging; MG, mammography; CT, computed tomography; US, ultrasound.