An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation

Abstract

Medical image segmentation aims to identify important or suspicious regions within medical images. However, many challenges are usually faced while developing networks for this type of analysis. First, preserving the original image resolution is of utmost importance for this task where identifying subtle features or abnormalities can significantly impact the accuracy of diagnosis. While introducing the dilated convolution improves the resolution of the convolutional neural network (CNN), it is not without shortcoming, i.e., the loss of local spatial resolution due to increased kernel sparsity in checkboard patterns. To address this shortcoming, we conceptualize a double-dilated convolution module for maintaining local spatial resolution while improving the receptive field size. Then, this approach is applied, as a proof-of-work, to tumor segmentation task in mammograms. In addition, our proposal also tackles the class imbalance problem, originating at the pixel level of the mammogram screenings, by identifying and selecting the best candidate among a number of potential loss functions to facilitate mass segmentation. We also carry out quantitative and qualitative evaluations of the interpretability of our proposal by leveraging Grad-CAM (Gradient weighted Class Activation Map). We also present a comparative performance evaluation with existing explainable techniques tailored for segmenting images. Moreover, an empirical assessment on lesion segmentation is conducted on mammogram samples from the INBreast dataset, both with and without incorporating our envisaged dilation module into CNN. The obtained results elucidate the effectiveness of our proposal based on mass segmentation performance measures, such as Dice similarity and Miss Detection rate. Our analysis also promotes using the Tversky Loss function in training pixel-imbalanced data and integrating Grad-CAM for explaining image segmentation results.

Keywords

• Medical image analysis
• tumor segmentation
• mammograms
• CAD
• deep learning
• CNN