Abstract: The present invention discloses a digestive system pathology image recognition method and system, and a computer storage medium. The method includes: acquiring image data to be detected; constructing and loading a first learning model, and executing regional traversal prediction on the image data, so as to obtain a plurality of predicted probability values; performing screening to obtain sub-region image data, which meets a preset assessment condition, so as to form an intermediate feature sequence; and constructing and loading a second learning model, executing traversal prediction on the intermediate feature sequence, and generating and outputting pieces of predicted sub-region image data and final probability values of the pieces of predicted sub-region image data.

Abstract: The present invention provides a deep learning-based cancer prognosis survival prediction method and a device, and a storage medium. The method includes: data acquisition: acquiring sample data, where the sample data comprising pathological image data and clinical data of a sample; data preprocessing; prediction model training: training and evaluating a prediction model to obtain an optimal prediction model; and risk prediction: performing risk prediction on a new sample on the basis of an optimal classifier model and the optimal prediction model. In the method, data features of the pathological image data and clinical data are unified, the prediction model is trained and evaluated to obtain the optimal prediction model, and prognosis risk evaluation is performed on the new sample to enhance the efficiency of diagnosis and treatment in the clinical field and improve the accuracy of risk evaluation results.

Abstract: A capsule endoscope image recognition method based on deep learning, and a device and a medium is provided. After a plurality of frames of continuously captured images are processed to form an image sequence of a specific format, multi-channel recognition is performed on the plurality of frames of images by means of a 3D convolutional neural network model, and a recognition result is then output in combination with a recognition probability of each channel, such that the image recognition precision is improved.