Deep multi-scale attentional features for medical image segmentation

Automatic segmentation of medical images is a difficult task in the field of computer vision owing to the various backgrounds, shapes, size, and colors of polyps or tumors. Despite the success of deep learning (DL)-based encoder & ndash;decoder architectures in medical image segmentation, these models have several disadvantages. First, an architecture such as U-Net cannot encode multi-scale semantic information at a different level on the decoder side. Second, it fails to reimpose the feature maps adeptly due to its limited capability on capturing long-range feature dependencies. In this study, we solve this problem by capturing multi-scale global feature maps, which forces the network to learn different semantic information at each scale. Further, we utilize the attention mechanism to suppress noise and the undesirable features, leading to a thorough restoration of contextual feature dependencies. Finally, we propose a novel method which leverages the compound scaled EfficientNet as a encoder backbone for efficient feature extraction and the UNet decoder to reconstruct the fine-grained details. We evaluated the proposed method using three different medical datasets: Kvasir-SEG, nuclei segmentation, and skin-lesion segmentation. The experimental results demonstrate that the proposed method takes an unassailable lead in terms of segmentation accuracy over the baseline models across different datasets and backbone architectures. Further, the proposed method strengthens the segmentation quality of varying shapes, object shapes, suppresses the noise, and leads to a better performance.& nbsp; (c) 2021 Elsevier B.V. All rights reserved. Superscript/Subscript Available

Automated summary

Automatic segmentation of medical images is a challenging task due to the diverse characteristics of polyps or tumors. This study addresses the limitations of existing deep learning models by capturing multi-scale global features and utilizing an attention mechanism to improve segmentation accuracy and suppress noise. The proposed method outperforms baseline models across different datasets and backbone architectures, enhancing segmentation quality and overall performance.