2results about How to "Complement each other" patented technology

The application is a medical image segmentation method based on multi-modal self-supervision. First, the multi-modal medical image of the lesion tissue is obtained, including A-mode image and B-mode image, and the image is preprocessed. Then, a cycle-consistent modal contrast domain translation network is constructed, including two generators and two discriminators. The generator is used to convert the image of one mode into the image of another mode, including an encoder, an intermediate shared module and a decoder. The discriminator is used to judge the source of the input. Then, the cycle-consistent modal contrast domain translation network is pre-trained, the training loss is calculated, and the loss function includes multi-modal semantic consistency loss, adversarial loss, cross-domain translation loss and cycle consistency loss. Finally, the A-mode segmentation network and the B-mode segmentation network are constructed, the pre-trained weights are migrated to the two segmentation networks, and the trained two segmentation networks are respectively used for medical image segmentation of the corresponding mode. The contrast cross-domain translation is used as a multi-modal self-supervised pre-training task to learn more comprehensive modal features, promote the network to better learn modal characteristics and common knowledge, and improve the segmentation ability.

The application discloses an obstacle avoidance target recognition method and system based on infrared image and radar point cloud fusion, relates to the technical field of automatic driving and intelligent transportation, solves the problems of inaccurate space alignment and insufficient feature interaction in the prior art, and realizes high-robustness obstacle avoidance target recognition in all-weather complex scenes. The method comprises the following steps: preprocessing near-infrared images and millimeter wave radar point cloud data collected synchronously, and obtaining two-dimensional boundary boxes of potential obstacles, texture semantic feature vectors and geometric motion feature vectors of candidate target point cloud clusters through a target detection network and a point cloud feature extraction network; based on an external parameter matrix pre-jointly calibrated, the texture semantic feature vectors and the geometric motion feature vectors are spatially associated to obtain associated feature pairs; based on the associated feature pairs, deep fusion features are obtained, and the deep fusion features are input into a prediction head to jointly predict and output fusion recognition results of obstacle avoidance targets.