49results about How to "Avoid tuning" patented technology

The invention discloses a total variation regularization depth expansion network method for hyperspectral image unmixing. The method comprises the following steps: inputting hyperspectral image data and an end member spectrum library; constructing a hyperspectral data unmixing model fusing a sparse constraint term and a total variation regularization term and an optimization objective function of the hyperspectral data unmixing model; converting the optimization objective function into an equivalent augmented Lagrangian form, and performing iterative solution on each variable; expanding an iteration process into a deep learnable network structure with a plurality of layers, and parameterizing hyper-parameters and a plurality of approximate operators in iteration into learnable network parameters; constructing a comprehensive loss function, and continuously optimizing network parameters by using back propagation and gradient descent methods; according to the method, total variation regularization prior is fused into a sparse unmixing model, the sparse unmixing model is expanded into a learnable end-to-end neural network through iterative solution, and the method has excellent unmixing precision and robustness for noise-containing data.

The invention discloses a multi-modal image analysis method and system for cancer diagnosis, and the method comprises the following steps: 1, repairing an original projection sequence through a pre-trained self-supervised U-Net network, and generating a repaired projection sequence; 2, carrying out multi-modal image space-time alignment and metabolic dynamics modeling based on the restored projection sequence and the preoperative image, and generating a registration image and an intraoperative metabolism distribution diagram; 3, generating an initial boundary diagram with a biological reasonable topological structure and a corresponding confidence map and uncertainty map; step 4, positioning a high-uncertainty region by integrating the uncertainty map, and feeding back to the repair process in the step 1 and the joint optimization network in the step 3 for targeted adjustment until the uncertainty meets a preset condition; and 5, carrying out topology integrity verification on the initial boundary diagram, and generating a diagnosis report containing spatial positioning excision suggestions and confidence level grading.