Abstract: An oligomer solution of a polymer electrolyte, a polymer electrolyte, and an electrochemical device are provided. The oligomer solution of the polymer electrolyte comprises monomers and an electrolyte solution, the monomers form polymer molecular chains through free radical polymerization, and the polymer molecule chain is cross-linked to form a polymer electrolyte with a three-dimensional network structure; the electrolyte solution comprises an organic solvent, which has strong mobility, permeates into pores of electrodes, promotes interface contact between the polymer electrolyte and the electrodes, improves the interface infiltration between the polymer electrolyte and the electrodes, increases the ion conductivity, facilitate lithium ion transmission, and enhances the electrochemical performance of a battery.

Abstract: A polymer, a polymer electrolyte and a lithium ion battery are provided. The polymer includes a main chain and side chains of the polymer, the side chains contain conjugated cyclic groups and epoxy multi-element cyclic groups; the conjugated cyclic group is selected from saturated or unsaturated C10-C20 conjugated aromatic groups; the epoxy multi-element cyclic group is selected from saturated or unsaturated C3-C8 epoxy multi-element cyclic groups; the main chain of the polymer is selected from polyalkoxy ether. The polymer, the polymer electrolyte and the lithium ion battery can improve interaction between the polymer electrolyte and an active substance in an electrode plate, increase the adhesion of the polymer electrolyte, avoid detachment occurring at an interface between the plate and the electrolyte, and improve the interface stability of the battery.

Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set.

Abstract: A polymer, a polymer electrolyte and a lithium ion battery are provided. The polymer includes a main chain and side chains of the polymer, the side chains contain conjugated cyclic groups and epoxy multi-element cyclic groups; the conjugated cyclic group is selected from saturated or unsaturated C10-C20 conjugated aromatic groups; the epoxy multi-element cyclic group is selected from saturated or unsaturated C3-C8 epoxy multi-element cyclic groups; the main chain of the polymer is selected from polyalkoxy ether. The polymer, the polymer electrolyte and the lithium ion battery can improve interaction between the polymer electrolyte and an active substance in an electrode plate, increase the adhesion of the polymer electrolyte, avoid detachment occurring at an interface between the plate and the electrolyte, and improve the interface stability of the battery.

Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set.

Abstract: The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.

Abstract: The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.