A deep ensemble medical image segmentation with novel sampling method and loss function

Medical image segmentation is a critical task in computer vision because of facilitating precise identification of regions of interest in medical images. This task plays an important role in disease diagnosis and treatment planning. In recent years, deep learning algorithms have exhibited remarkable performance in this domain. However, it is important to note that there are still unresolved issues, including challenges related to class imbalance and achieving higher levels of accuracy. Considering the challenges, we propose a novel approach to the semantic segmentation of medical images. In this study, a new sampling method to handle class imbalance in the medical datasets is proposed that ensures a comprehensive understanding of both abnormal tissues and background characteristics. Additionally, we propose a novel loss function inspired by exponential loss, which operates at the pixel level. To enhance segmentation performance further, we present an ensemble model comprising two UNet models with ResNet backbone. The initial model is trained on the primary dataset, while the second model is trained on the dataset obtained through our sampling method. The predictions of both models are combined using an ensemble model. We have assessed the effectiveness of our approach using three publicly available datasets: Kvasir-SEG, FLAIR MRI Low-Grade Glioma (LGG), and ISIC 2018 datasets. In our evaluation, we have compared the performance of our loss function against four different loss functions. Furthermore, we have showcased the excellence of our approach by comparing it with various state-of-the-art methods.