Abstract
Accurate rectal staging has great significance for surgery. However, it is difficult for radiologists to diagnose because of the heavy workload and the shortage of experts. In this paper, we proposed a deep neural network to automatically identify the rectal cancer T-stage. The network combines 3D DenseNet with 3D attention proposal network, which can use the 3D image as training data and take full advantage of the latent relation of each slice. The APN can distinguish slight differences of each rectal cancer T-stage, which use the last convolution layer to create the attention region that can generate multi-scale images. Moreover, the APN can focus on the tumor region and use a rectangle box to identify the related region, which is approximately regarded as network interpretation. The experiment is conducted on MRI images of 254 patients with rectal cancer of T1, T2, T3 stage. In contrast experiment, our proposed method outperforms other methods, so it is indicated that our method is effective.
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This work is supported by the Key Field Special Project of Guangdong Provincial Department of Education with No. 2021ZDZX1029.
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Chen, C., Li, K. (2022). Study of Weakly Supervised Learning: 3D DenseNet and 3D APN for Rectal Cancer T-staging. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_24
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DOI: https://doi.org/10.1007/978-981-19-4109-2_24
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