Abstract: A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

Abstract: A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

Abstract: A method for differentiable architecture search based on a hierarchical grouping mechanism includes: obtaining a target dataset; selecting a set number of normal cells and two reduction cells; enabling the reduction cells to be located at ? and ? of all cells, respectively, and then concatenating all cells to form an initial search network; training the initial search network to optimize a cost function to complete a one-level search or a two-level search, wherein in the one-level search, control weight parameters are shared by the inter-group operations and are shared by the intra-group operations among the cells; and in the two-level search, the control weight parameters are shared by the inter-group operations and are not shared by the intra-group operations, obtaining normal cells and reduction cells based on the one-level search or the two-level search; and constructing a target network using the normal cells and the reduction cells.