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

• DOI: 10.20944/preprints202310.0572.v1
• Publication Date: 2023-10-16T02:16:26Z
• Authors: Loris Nanni; Alessandra Lumini; Carlo Fantozzi
• Keywords: deep learning; ensembles; segmentation; transformers; 0302 clinical medicine; segmentation; 0202 electrical engineering, electronic engineering, information engineering; deep learning; ensembles; transformers; Information technology; T58.5-58.64

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, data augmentation method or the learning rate strategy. Our proposed ensemble, which is based on the simple average rule, demonstrates exceptional performance on several datasets. All the resources used in this study are available online at the following GitHub repository: https://github.com/LorisNanni.