Kappa Loss For Skin Lesion Segmentation In Fully Convolutional Network

Skin melanoma represents a major health issue. Today, diagnosis and follow-up can rely on computer-aided diagnosis tools, to help dermatologists segment and quantitatively describe the image content. In particular, deep convolutional neural networks (CNN) have lately been become the state-of-the-art in automated medical image segmentation. The loss function plays an important role in CNN in the backpropagation process. In this work, we propose a metric-inspired loss function, based on the Kappa index. Unlike the Dice loss, a standard loss used in image segmentation CNN, the Kappa loss takes into account all the pixels in the image, including the true negative - we believe this can improve the accuracy of the evaluation process between prediction and ground truth. We demonstrate the differentiability of the Kappa loss and present some results on six public datasets of skin lesion. Experiments have shown promising results in skin lesion segmentation.