6results about How to "Effective study" 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 a feature automatic extraction method around a tooth boundary and a lesion recognition method, relates to the technical field of oral medicine, and comprises the following steps: acquiring a digital image of an oral X-ray apical film to be processed; performing automatic segmentation and contour extraction on a target tooth in the digital image of the oral X-ray apical film to obtain a tooth contour line of the target tooth; performing normalization processing on the tooth contour line; and traversing each boundary pixel point on the tooth contour line to generate an edge band feature map of the target tooth. The application can effectively extract local features highly related to lesions, especially features of a tooth boundary region, from the oral X-ray apical film, so that the recognition capability for early micro-lesions is improved.

This application provides a method, electronic device, storage medium, and product for updating parameters of a quantum circuit. The method includes: updating a first trainable parameter of a current noisy quantum circuit based on a current policy network to obtain at least one variation path of the first trainable parameter; determining a reward value based on each variation path and a reward function; the accuracy of the reward function and the output of the noisy quantum circuit is related to the degree of influence of noise on the output; updating a second trainable parameter of the policy network based on the reward value; and repeating the above steps until the policy network reaches a preset performance requirement. In this way, the policy network, through reinforcement learning, automatically adjusts the parameterized quantum gates in the noisy quantum circuit to achieve dynamic adaptation of the quantum circuit to noise. By dynamically adjusting the trainable parameters of the quantum circuit through a unified policy network, it is not necessary to model and train separately for each type of noise, thus improving processing efficiency.

This invention provides an ECG signal classification system based on deep learning neural networks. It includes a common feature extraction module using a convolutional neural network (CNN) to extract shallow features from ECG signals; a domain-invariant branch module using a CNN to extract features from the shallow features and perform feature matching for the source domain by minimizing the classification loss, obtaining a prediction; a domain-specific branch module using a CNN with LMMD loss to perform feature matching between each pair of source and target domains for the input shallow features, obtaining n predictions; and a fusion module assigning different confidence levels to each sub-branch and combining the n+1 predictions to obtain the final prediction. The multi-branch network and multi-source unsupervised domain adaptation of this invention effectively learn the general features of ECG signals, enabling the knowledge learned from the source dataset to be applied to the unknown target dataset. Furthermore, by using a prior classifier-based fusion strategy, multiple predictions are organically combined to obtain the final result, further improving the model's generalization performance.

This disclosure relates to a method, apparatus, device, and medium for processing data stream requests. The method provided in the embodiments of this disclosure determines the data source based on the refresh type in the data stream request and pulls a candidate set from the data source. This achieves filtering of the data source through the refresh type, thereby ensuring that the data content in the candidate set pulled each time is different. At the same time, based on the state transition conditions satisfied by the key information of the candidate set, the refresh type is switched, so that when the server receives the next data stream request sent by the client, it pulls the candidate set again according to the refresh type after the switch. This does not take up too much browsing time for the operator and can ensure that the operator can effectively learn from the pushed data content, thus improving the operator's browsing experience.

This invention provides an information recommendation method, apparatus, device, and storage medium. The method involves acquiring sequence feature data corresponding to information to be recommended, where the sequence feature data is collected when a user interacts with the information. The method then determines the category corresponding to the sequence feature data; determines the attention information corresponding to the sequence feature data for each category; determines the relevance information between the information to be recommended and the user based on the sequence feature data and the attention information corresponding to the category; and recommends the information to the user based on the relevance information. This invention, while ensuring sufficient cross-category interaction, avoids the risk of information intrusion between sequence feature data from different categories, thereby enabling more accurate modeling of user interests and improving the accuracy of the recommendation algorithm.