Deep Kernel Learning with Application to Medical Image Annotation

Deep learning methods achieve significant success in many machine learning problems. By increasing the model depth, deep model can learn very complex functions from large scale dataset. Kernel method induce a mapping from input space to certain high dimension feature space via a kernel function. Kernel method can achieve good generalization performance even with small training datasets. A proper kernel function would dramatically affect the model performance. To learn an effective kernel function, we propose a deep learning based multiple-layer multiple-kernel learning algorithm, utilizing the learning ability of deep model to find the best combination of a base set of structured kernel functions. The proposed method updates the weights of a kernel network by optimizing a metric based on the performance of a SVM classifier with current learned kernels, which is similar to the training procedure of deep neural network. The proposed method is applied to the problem of medical image annotation and achieves superior results with comparison to current state-of-the-art methods.