Mammogram Tumor Segmentation with Preserved Local Resolution: An Explainable AI System

Medical image segmentation is a crucial component of computer-aided diagnosis (CAD) systems, as it aids in identifying important areas in medical images. In order to achieve optimal segmentation results, it is important to preserve the resolution of the input image. The dilated convolution module was introduced to maintain resolution across layers of a deep convolutional neural network by increasing the receptive field exponentially while keeping the parameters increase linearly. However, one drawback of using dilated convolution is that it can result in local spatial resolution loss by increasing the sparsity of the kernel in checkboard patterns. This work proposes a double-dilated convolution module to maintain local spatial resolution in medical image segmentation tasks while having a large receptive field. The module is applied to tumor segmentation in breast cancer mammograms using the state-of-art Deeplabv3+ network. The study also evaluates the developed models with the Gradient weighted Class Activation Map (Grad-CAM) and compares the performance of lesion segmentation networks on mammogram screenings from the INBreast dataset before and after using the proposed dilation module. The results show that the proposed module effectively improves the segmentation performance.