Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets

Abstract

Computer aided detection (CADe) and computer aided diagnostic (CADx) systems are ongoing research areas for identifying lesions among complex inner structures with different pixel intensities, and for medical image classification. There are several techniques available for breast cancer detection and diagnosis using CADe and CADx systems. However, some of these systems are not accurate enough or suffer from lack of sufficient data. For example, mammography is the most commonly used breast cancer detection technique, and there are several CADe and CADx systems based on mammography, because of the huge dataset that is publicly available. But, the number of cancers escaping detection with mammography is substantial, particularly in dense-breasted women. On the other hand, digital breast tomosynthesis (DBT) is a new imaging technique, which alleviates the limitations of the mammography technique. However, the collections of huge amounts of the DBT images are difficult as it is not publicly available. In such cases, the concept of transfer learning can be employed. The knowledge learned from a trained source domain task, whose dataset is readily available, is transferred to improve the learning in the target domain task, whose dataset may be scarce. In this paper, a two-level framework is developed for the classification of the DBT datasets. A basic multilevel transfer learning (MLTL) based framework is proposed to use the knowledge learned from general non-medical image datasets and the mammography dataset, to train and classify the target DBT dataset. A feature extraction based transfer learning (FETL) framework is proposed to further improve the classification performance of the MLTL based framework. The FETL framework looks at three different feature extraction techniques to augment the MLTL based framework performance. The area under receiver operating characteristic (ROC) curve of value 0.89 is obtained, with just 2.08% of the source domain (non-medical) dataset, 5.09% of the intermediate domain (mammography) dataset, and 3.94% of the target domain (DBT) dataset, when compared to the dataset reported in literature.

Keywords: Deep learning; Digital breast tomosynthesis; Feature fusion; GLCM; Transfer learning.

Fig. 1

MLTL framework

Fig. 2

FETL framework

Fig. 3

Area under the ROC curve of the intertune MedCNN (intermediate mammography domain)

Fig. 4

Area under the ROC curve of the Fine-tune MedCNN (target DBT domain)

Fig. 5

Canny edge detection output of the DBT images

Fig. 6

Area under the ROC curve of the fine-tune MedCNN using CNCF algorithm

Fig. 7

GLCM output feature images of the DBT images

Fig. 8

Area under the ROC curve of the fine-tune MedCNN using GLCM algorithm

Fig. 9

Area under the ROC curve of the fine-tune MedCNN using MIP algorithm