Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
Source abstract
Abstract
Image segmentation is a basic technology in the field of image processing and computer vision. Medical image segmentation is an important application field of image segmentation and plays an increasingly important role in clinical diagnosis and treatment. Deep learning has made great progress in medical image segmentation. In this paper, we proposed Residual-Attention UNet++, which is an extension of the UNet++ model with a residual unit and attention mechanism. Firstly, the residual unit improves the degradation problem. Secondly, the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task. Three medical image datasets such as skin cancer, cell nuclei, and coronary artery in angiography were used to validate the proposed model. The results showed that the Residual-Attention UNet++ achieved superior evaluation scores with an Intersection over Union (IoU) of 82.32%, and a dice coefficient of 88.59% with the skin cancer dataset, a dice coefficient of 85.91%, and an IoU of 87.74% with the cell nuclei dataset and a dice coefficient of 72.48%, and an IoU of 66.57% with the angiography dataset.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Radiomics and Machine Learning in Medical Imaging · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Minzu University of China, Beijing 100081, China
References (37)
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