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The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.

The problem of classifying imbalanced datasets has drawn a significant amount of interest from academia and industry. In this paper, we propose a modified support vector machine (SVM) approach using conformal kernel transformation to address the class imbalance problem. The proposed method first uses standard SVM algorithm to obtain an approximate hyperplane. And then, we give a kernel function and compute its parameters using the chi-square test. Finally, an experimental analysis is carried out with a wide range of highly imbalanced datasets over the proposal and several other methods. The results show that our proposal outperforms previously proposed methods.

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