Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation

Abstract

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g., by varying the loss function, the data augmentation method, or the learning rate strategy. Our proposed ensemble, which uses a simple averaging rule, demonstrates exceptional performance across multiple datasets. Notably, compared to prior state-of-the-art methods, our ensemble consistently shows improvements in the well-studied polyp segmentation problem. This problem involves the precise delineation and identification of polyps within medical images, and our approach showcases noteworthy advancements in this domain, obtaining an average Dice of 0.887, which outperforms the current SOTA with an average Dice of 0.885.