Weakly-supervised US breast tumor characterization and localization with a box convolution network

In US breast tumor diagnosis, machine learning approaches for the malignancy classification and the mass localization have been attracting many researchers to improve the diagnostic sensitivity and specificity while reducing the image interpretation time. Recently, fully-supervised deep learning methods showed their promising results in those tasks. However, the full supervision for the localization requires human efforts and time to annotate ground truth regions. In this paper, we present a weakly-supervised deep network which can localize breast masses in US images from only diagnostic labels (i.e., malignant and benign). Specifically, we exploit a flexible convolution method, which learns the size and offset of the convolution kernel, in the classification network to detect more relevant regions of breast masses against their various size and shape. Experimental results show that the proposed network outperform conventional CNN models, such as VGG-16 and VGG-16 with dilated convolution. The proposed model achieved 89.03% in the binary classification accuracy. To evaluate the localization performance with weakly-supervised manners, we also compared class activation maps for each instance with manual masks of breast mass in terms of the Dice similarity coefficient and localization recall. The experimental results also demonstrate that the deep network with the adjustable convolution layers can clinically relevant features of breast mass and its surrounding area for both benign and malignant cases.