ICADx: interpretable computer aided diagnosis of breast masses

Abstract

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network

“…The The classification results show that our second stage properly infers the tumor shape from the binary mask of the breast tumor, which was obtained from the first stage (cGAN segmentation). Hence, we have empirically shown that our CNN is focusing its learning on the morphological structure of the breast tumor, while the rest of approaches ( [10], [11], [45], [46]) rely on the original pixel variations of the input mammogram to make the same inference. Moreover, in [32] they used a hybrid strategy in which they include the pixel variability within the mask of breast tumor region to retain the intensity and texture information.…”

“…We have computed the overall accuracy of each method by averaging the correct predictions (i.e., true positive) of the four classes, weighted with respect to the number of samples per class. As shown in Table 3, our classifier yields an overall accuracy of 80%, outperforming the second best results [11,12] Basal-like 5 10 9 13 37…”

“…Previously, Singh et al [12] proposed a multi-class CNN to categorize the tumor shapes into four classes as in [11] from the public dataset DDSM 1 .…”

“…In the literature, most methods attempted to directly categorize the shape using breast tumor intensity, texture, boundary, etc. ( [10,45,46,11]), which increase computational complexity. We simplify the problem by extracting morphological features from binary masks.…”

“…In the second stage, the binary mask is classified to a shape type (irregular, lobular, oval and round). Unlike traditional object classifiers [10], [11] that use texture, intensity or edge information, our method is forced to learn only morphological features from the binary masks. To be more specific, we present a thorough improvement of our previous work [12].…”