Addressing the Class Imbalance Problem in Medical Image Segmentation via Accelerated Tversky Loss Function

Abstract

Image segmentation in the medical domain has gained a lot of research interest in recent years with the advancements in deep learning algorithms and related technologies. Medical image datasets are often imbalanced and to handle the imbalance problem, deep learning models are equipped with modified loss functions to effectively penalize the training weights for false predictions and conduct unbiased learning. Recent works have introduced various loss functions suitable for certain scenarios of segmentation. In this paper, we have explored the existing loss functions that are widely used for medical image segmentation, following which an accelerated Tversky loss (ATL) function is proposed that uses log cosh function to better optimize the gradients. The no-new U-Net (nn-Unet) model is adopted as the base model to validate the behaviour of the loss functions by using the standard benchmark segmentation performance metrics. To establish the robustness and effectiveness of the loss functions, multiple datasets are adopted, where ATL function illustrated better performance with faster convergence and better mask generation.