Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of diseases and how it has revolutionized the medical imaging field. Furthermore, we examine the most recent DL techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in medical imaging. Lastly, we provide insights into the future of DL in medical imaging, highlighting its potential advancements and challenges.

A highly efficient deep-blue OLED with a FHWM of 35 nm and CIE coordinate of (0.161, 0.046) was obtained based on the fully twisting donor–acceptor molecule TPA–PIM.

The coherent processing and superresolution of multi-band radar data from multiple spatially collocated radars is addressed by utilizing a sparse representation technique in this paper. Firstly a parametric model based on geometrical theory of diffraction (GTD) is adopted to construct a redundant dictionary and provide a good match to the scattering mechanism. Then weights of dictionary atoms are computed by using a fast sparse Bayesian learning algorithm. Meanwhile, a multilevel dynamic dictionary is proposed to improve the accuracy and avoid a vast number of computations due to a large dictionary matrix. The final weights and dictionary atoms are adopted to interpolate between and extrapolate outside of the measurement subbands. Furthermore, a procedure for coherent processing is presented to compensate for the lack of mutual coherence among multiband data from multiple spatially collocated radars. The capability and robustness of the proposed method are validated by applying it to the analytical, simulated and static-range data.