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Prediction of lymph node metastasis in CT based on multi-scale attention fully convolutional network | IEEE Conference Publication | IEEE Xplore

Prediction of lymph node metastasis in CT based on multi-scale attention fully convolutional network


Abstract:

With the application of artificial intelligence technology in the medical field, the use of deep learning methods to predict lymph node (LN) metastasis from Computed Tomo...Show More

Abstract:

With the application of artificial intelligence technology in the medical field, the use of deep learning methods to predict lymph node (LN) metastasis from Computed Tomography (CT) images has become one of the important studies in adjuvant cancer treatment. Although many studies have made some progress, the large difference in LNs size has always limited the performance of traditional convolutional neural network methods. In this paper, we propose a multi-scale attention fully convolutional network for LN metastasis prediction in CT images. We aim to make the network compatible with LNs of different sizes to improve the overall performance of the network. First, the network extracts image features from multiscale input data. Then, we use an attention-based fusion module to study the relationship between features at different scales and adaptively fuse image features. Furthermore, a feature consistency loss is proposed by us to enhance the similarity between image features at different scales. The experimental results show that our proposed network achieves the best performance with the accuracy 92.37%, the sensitivity 90.21% and the specificity 95.71%, which outperforms several state-of the-art methods.
Date of Conference: 09-12 October 2022
Date Added to IEEE Xplore: 18 November 2022
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Conference Location: Prague, Czech Republic

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