Artificial intelligence (AI) algorithms, especially deep learning methods have proven to be successful in many medical imaging applications. Computerized breast cancer image analysis can improve diagnosis accuracy. Digital Breast Tomosynthesis (DBT) imaging is a new modality and more advantageous compared to classical digital mammography (DM). Therefore, development of new deep learning algorithms compatible with DBT modality are potent to improve DBT imaging reading time efficiency and increase accuracy for breast cancer diagnosis when used as additional tool for radiologists. In this work, we aimed to build a 3D deep learning model to distinguish malignancy and benign breasts using DBT images. We also investigated effects of different loss functions in our deep learning models. We implemented and evaluated our method on a large data set of 546 patients (205 malignancy and 341 benign). Our results showed that different loss functions lead to an influence on the models performance in our classification tasks, and specific loss function may be selected or customized to adjust a specific performance metric for concrete applications.
Computer-aided diagnosis plays an important role in clinical image diagnosis. Current clinical image classification tasks usually focus on binary classification, which need to collect samples for both the positive and negative classes in order to train a binary classifier. However, in many clinical scenarios, there may have many more samples in one class than in the other class, which results in the problem of data imbalance. Data imbalance is a severe problem that can substantially influence the performance of binary-class machine learning models. To address this issue, one-class classification, which focuses on learning features from the samples of one given class, has been proposed. In this work, we assess the one-class support vector machine (OCSVM) to solve the classification tasks on two highly imbalanced datasets, namely, space-occupying kidney lesions (including renal cell carcinoma and benign) data and breast cancer distant metastasis/non-metastasis imaging data. Experimental results show that the OCSVM exhibits promising performance compared to binary-class and other one-class classification methods.