Abstract
The medical image is often noisy, which makes it difficult to extract the image features from the medical image segmentation model. Because the noise is often generated randomly, it is difficult to use supervised information for denoising. In this paper, we focus on this challenging problem and propose an IM-Net algorithm for medical image segmentation based on mutual information maximization. The IM-Net can remove the noise and therefore improve the quality of the extracted feature by maximizing the mutual information between the extracted feature and the input image. IM-Net uses the Binary Cross Entropy with Logits Estimation to approach the true value of mutual information and uses a bilinear interpolation function as a discriminator to maximize the mutual information estimator. Extensive experiments are conducted and the IM-Net is compared with different methods to demonstrate the effectiveness of our model. Experimental results show that the training efficiency and segmentation precision are greatly improved.
This work was supported in part by National Natural Science Foundation of China (No. 61806097); College Students Entrepreneurship Training Program 2019 (No. S20190025). The study was funded by Shanghai Science and Technology Committee (17511104200, 18411952100, 17411953500).
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Sun, Y., Yuan, P. (2020). IM-Net: Semantic Segmentation Algorithm for Medical Images Based on Mutual Information Maximization. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_35
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