Feng Lin

TL;DR: Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction from single-lead electrocardiograph (ECG) signals.

TL;DR: It is demonstrated that anisotropic stiffness gradient (ASG) has the ability to elicit directional migration of cells, essentially depending on local stiffness gradients and the corresponding absolute stiffness values.

TL;DR: The noise reduction of the network is improved through a supervised fine-tuning of the parameters of the proposed deep network model, thus increasing the robustness of the whole system in clinical applications and preserving useful information while removing noises from the ECG signal.

TL;DR: A real-time virtual biopsy technique that can complement current diagnostic techniques and aid in targeted biopsy for better clinical outcomes is aim toward.

TL;DR: The new method adopts the discriminative information from the labeled data to construct local patches and aligns these patches to get the optimal low dimensional subspace for each modality, and adopts an alternating and iterative optimization algorithm to explore the complementary characteristics of different modalities.