2results about How to "Implement feature extraction" patented technology

The application discloses a multi-target detection method in a complex electromagnetic interference environment for a pulse Doppler radar, a joint reverse diffusion process is introduced to jointly update interference samples and amplitude samples, and thus the problems of target signals being covered or submerged by interference signals, rising of a detection false alarm rate, and difficulty in effectively separating interference from targets can be overcome; a denoising score matching criterion is introduced to train a neural network of an interference score function, so as to solve the problems of high interference intensity and complex structure in multi-target detection in a complex electromagnetic interference environment; and a sparse Bayesian learning method is introduced to adaptively model a sparse structure of multi-target echo signals. The application further provides a multi-target detection device in a complex electromagnetic interference environment. The method provided by the application can realize modeling and suppression of multiple types of interference, and simultaneously improve the detection probability of multiple low observable targets in the distance and Doppler dimension, the interference suppression capability, and the system robustness in a complex electromagnetic environment.

This invention discloses a method, device, and system for thymoma detection and classification based on a prediction model, relating to the field of thymoma detection and classification technology. It addresses the problem of the inability to perform detailed and accurate verification and classification of the prediction results of the prediction model. The method includes: analyzing the grayscale differences between different pixels in a medical image based on grayscale values; analyzing the positional relationship between lesion pixels and the image coordinate system based on the coordinates of different pixels; analyzing the undetermined thymoma region in the image to be identified based on the reference grayscale deviation interval and the included angle interval to obtain the true thymoma region and the pseudo-thymoma region; analyzing the adhesion type between the true thymoma region and the tissue region to determine whether the adhesion type of the true thymoma region is a non-adhesive thymoma region or an adherent thymoma region; and outputting the classification result of the image to be identified based on different adhesion types. This invention achieves detailed and accurate verification and classification of the prediction results corresponding to the prediction model.