Non-uniform Sampling-Based Breast Cancer Classification

The emergence of deep learning models and their remarkable success in visual object recognition and detection have fueled the medical imaging community’s interest in integrating these algorithms to improve medical screening and diagnosis. However, natural images, which have been the main focus of deep learning models, and medical images, such as mammograms, have fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions. To fit these images to deep learning approaches, they must be heavily downsampled. Otherwise, models that address high-resolution mammograms require many exams and complex architectures. Spatially resizing mammograms leads to losing discriminative details that are essential for accurate diagnosis. To address this limitation, we develop an approach to exploit the relative importance of pixels in mammograms by conducting non-uniform sampling based on task-salient regions generated by a convolutional network. Classification results demonstrate that non-uniformly sampled images preserve discriminant features requiring lower resolutions to outperform their uniformly sampled counterparts.