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The simulation and experiment with the dual-wavelength EMAT proved the correctness of the proposed method. Finally, an experiment with a three-wavelength EMAT demonstrated the feasibility of the proposed method in designing multiwavelength EMATs.While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this paper, we propose a self-supervised convolutional neural network (CNN) frame-work to leverage unlabel