Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

Abstract

Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges-as well as outlooks-are discussed.

Keywords: breast cancer; transfer learning; ultrasound.

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.