Nanni, L.; Fantozzi, C.; Loreggia, A.; Lumini, A. Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors2023, 23, 4688.
Nanni, L.; Fantozzi, C.; Loreggia, A.; Lumini, A. Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors 2023, 23, 4688.
Nanni, L.; Fantozzi, C.; Loreggia, A.; Lumini, A. Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors2023, 23, 4688.
Nanni, L.; Fantozzi, C.; Loreggia, A.; Lumini, A. Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors 2023, 23, 4688.
Abstract
In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects’ boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and we develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combine different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask which is more suitable for combining transformers in an ensemble. In our extensive experimental evaluation, the proposed ensembles exhibit state-of-the-art performance.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
