Abstract: A method for training a medical image report generation model that includes a visual feature extraction network, an encoding network, and a decoding network. The method includes: acquiring a sample medical image; extracting visual feature information of on a sample medical image through the visual feature extraction network, to obtain a visual feature sequence; concatenating a self-learning label based on the visual feature sequence, to obtain input information about the encoding network; encoding the input information through the encoding network, to obtain a visual encoding feature vector and an output task result; decoding the visual encoding feature vector through the decoding network, to obtain an output image report; and calculating a loss of the model based on the output image report and the output task result, and adjusting a parameter of the medical image report generation model according to the total loss function value.

Abstract: A method for training a medical image report generation model that includes a visual feature extraction network, an encoding network, and a decoding network. The method includes: acquiring a sample medical image; extracting visual feature information of on a sample medical image through the visual feature extraction network, to obtain a visual feature sequence; concatenating a self-learning label based on the visual feature sequence, to obtain input information about the encoding network; encoding the input information through the encoding network, to obtain a visual encoding feature vector and an output task result; decoding the visual encoding feature vector through the decoding network, to obtain an output image report; and calculating a loss of the model based on the output image report and the output task result, and adjusting a parameter of the medical image report generation model according to the total loss function value.