2results about How to "Strong memory" patented technology

The application belongs to the field of medical image recognition and relates to an esophageal lesion image recognition method based on a deep neural network, which comprises the following steps: step 1, processing actual digestive endoscopy video data to construct training data; step 2, constructing a deep neural network model and training the deep neural network model through the training data to obtain an early esophageal cancer recognition model; step 3, inputting digestive endoscopy video data to be detected into the early esophageal cancer recognition model, and the early esophageal cancer recognition model judging whether there is a suspected esophageal cancer lesion and outputting a lesion judgment result; the lesion judgment result comprising a spatial position and a progression stage of the lesion; the method models the time sequence dynamics of lesion characteristics in the endoscopy recognition process, and combines a graph neural network to depict the feature correlation of early esophageal squamous cell carcinoma under multi-view conditions, so as to improve the recognition accuracy, consistency and robustness of the model in a real clinical application scenario.

The present application relates to the field of automatic driving strategy evolution, and in particular to an end-to-end automatic driving strategy evolution method based on supervised-reinforcement joint fine-tuning, wherein the data samples of the user using the automatic driving system are divided into performance defect samples, physiological and psychological feedback samples, partial intervention samples and complete takeover samples according to feedback levels, the end-to-end automatic driving strategy evolution is realized by using a reinforcement learning fine-tuning method for the performance defect samples and the physiological and psychological feedback samples, the end-to-end automatic driving strategy evolution is realized by using a supervised learning fine-tuning method for the partial intervention samples and the complete takeover samples, a dynamic sample library is established, the memory samples are dynamically updated according to the difference degree of the samples, the memory sample loss is weighted and calculated according to the difference degree between the memory samples and the samples currently used for fine-tuning training, and finally the memory sample loss is used to perform soft constraint on the reinforcement learning fine-tuning and the supervised learning fine-tuning. The present application makes full use of multi-scene data sample flow and can continuously learn.