3 results about "Multinomial logistic regression" patented technology

Disclosed in the present invention is a physician-interpretable ultrasound-based breast cancer pathological subtype detection system, comprising: a segmentation module, which uses a U-Net network and incorporates a foreground optimization network to segment a breast and a nodule; a texture channel, which extracts a texture channel feature; an edge channel, which extracts boundary information; a position channel, which extracts position information, so as to quantize the overall relationship between the nodule and the boundary information of the breast to extract a breast nodule growth position feature; a shape channel, which calculates an edge aspect ratio on the basis of a segmented nodule region, so as to obtain a predicted interval value as a shape channel feature; an echo channel, which calculates the echo difference between the interior and exterior of the nodule to serve as an echo channel feature value; and a joint prediction module, which uses a multinomial logistic regression model to classify and predict breast cancer pathological subtypes on the basis of output features of the texture channel, the edge channel, the echo channel, the shape channel and the position channel. An artificial intelligence diagnosis tool that is interpretable to physicians is built, thereby providing an automated theoretical basis for clinical diagnosis and decision-making.

The invention belongs to the field of deep learning and remote sensing image processing, and particularly discloses a hyperspectral few-sample classification network construction method and a hyperspectral ground feature classification method, and the method comprises the steps: obtaining a hyperspectral remote sensing image and sample label data thereof; performing cross-domain reconstruction and spectral curve extraction, and filtering out common information among categories through eigenvalue decomposition to obtain a discretized spectral curve; extracting physical invariance features, and taking the physical invariance features as constraint rules to generate virtual samples; the reconstructed hyperspectral data and the spectrum self-supervision auxiliary information are input into a double-branch variational automatic encoder network, multi-loss constraint of cross reconstruction is carried out, and the hyperspectral data and the spectrum self-supervision auxiliary information of the same category show greater similarity in a potential space; and outputting a final surface feature prediction result based on the multinomial logistic regression classifier, and completing the construction of the hyperspectral few-sample classification network. According to the invention, high-precision and high-robustness hyperspectral ground feature classification can be realized under the condition of sample scarcity.

The application belongs to the field of deep learning and remote sensing image processing, and specifically discloses a hyperspectral few-shot classification network construction method and a hyperspectral feature classification method, which comprises the following steps: acquiring hyperspectral remote sensing images and sample label data thereof; performing cross-domain reconstruction and spectrum curve extraction, and filtering common information between categories through eigenvalue decomposition to obtain discrete spectrum curves; extracting physical invariance features and taking them as constraint rules to generate virtual samples; inputting the reconstructed hyperspectral data and spectrum self-supervised auxiliary information into a double-branch variational autoencoder network to perform multi-loss constraint of cross reconstruction, so that the hyperspectral data and the spectrum self-supervised auxiliary information of the same category show greater similarity in the latent space; and outputting final feature prediction results based on a multinomial logistic regression classifier to complete the construction of the hyperspectral few-shot classification network. The application can realize high-precision and high-robustness hyperspectral feature classification under the condition of sample scarcity.