Abstract: A method, an electronic device, and a computer-readable storage medium for performing identification based on multi-modal data are provided. The method includes the following. A data string is obtained. The data string is fed into a first deep neural network to generate a first feature map. Multi-dimensional data is generated based on the data string. The multi-dimensional data is fed into a second deep neural network to generate a second feature map. At least the first feature map and the second feature map are fused into a specific feature vector. The specific feature vector is fed into a machine learning model. The machine learning model outputs an identification result corresponding to the data string in response to the specific feature vector.

Abstract: A medical image analysis method includes: reading an original medical image; performing image classification and object detection on the original medical image to generate a first classification result and a plurality of object detection results by a plurality of complementary artificial intelligence (AI) models; performing object feature integration and transformation on a first detection result and a second detection result among the object detection results to generate a transformation result by a features integration and transformation module; and performing machine learning on the first classification result and the transformation result to generate an image interpretation result by a machine learning module and display the image interpretation result.

Abstract: A medical image analysis method includes: reading an original medical image; performing image classification and object detection on the original medical image to generate a first classification result and a plurality of object detection results by a plurality of complementary artificial intelligence (AI) models; performing object feature integration and transformation on a first detection result and a second detection result among the object detection results to generate a transformation result by a features integration and transformation module; and performing machine learning on the first classification result and the transformation result to generate an image interpretation result by a machine learning module and display the image interpretation result.