Abstract: The present disclosure discloses a method and apparatus for analyzing medical image. A specific embodiment of the method includes: acquiring medical image data; generating multi-scale decision sample data based on the medical image data; inputting the multi-scale decision sample data into a deep neural network model to obtain an auxiliary diagnosis data of the medical image, the deep neural network model being trained according to a consistency principle between multi-scale training sample data and an output result of the deep neural network model. In the embodiment, a multi-scale training sample is used to accelerate the training process of the deep neural network model, thus the auxiliary diagnosis decision process can be accelerated, while the accuracy of the trained deep neural network model of the embodiment is improved according to a consistency principle of data between different scales and output results, thereby improving the accuracy of auxiliary diagnosis decision.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for acquiring information. A specific embodiment of the method includes: acquiring a fundus image; introducing the fundus image into a pre-trained disease grading model to obtain disease grading information, the disease grading model being used for extracting characteristic information from a lesion image included in the fundus image, and generating disease grading information based on the extracted characteristic information, the disease grading information including grade information of a disease, a lesion type, a lesion location, and a number of lesions included by the disease; and constructing output information using the disease grading information. This embodiment improves the accuracy of grading information.

Abstract: An embodiment of the present disclosure discloses a method and apparatus for annotating a medical image. An embodiment of the method comprises: acquiring a to-be-annotated medical image; annotating classification information for the to-be-annotated medical image, wherein the classification information comprises a category of a diagnosis result and a grade of the diagnosis result corresponding to the medical image; processing the to-be-annotated medical image using a pre-trained lesion area detection model, framing a lesion area in the to-be-annotated medical image, and annotating a lesion type of the lesion area, to enable the to-be-annotated medical image to be annotated with the lesion area and the lesion type of the lesion area; and splitting the framed lesion area from the to-be-annotated medical image with the framed lesion area to form a split image of the to-be-annotated medical image, to enable the to-be-annotated medical image to be annotated with the split image.

Abstract: The present disclosure discloses a method and apparatus for acquiring information. An implementation of the method comprises: acquiring a fundus image; introducing the fundus image into a pre-trained pathological classification model to obtain pathological classification information, wherein the pathological classification model is used for characterizing correspondence between a pathological area image contained from the fundus image and the pathological classification information, and the pathological classification information comprises at least one of diabetic retinopathy classification information and diabetic macular edema classification information; and establishing output information based on the pathological classification information.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for acquiring information. A specific embodiment of the method includes: acquiring a fundus image; introducing the fundus image into a pre-trained disease grading model to obtain disease grading information, the disease grading model being used for extracting characteristic information from a lesion image included in the fundus image, and generating disease grading information based on the extracted characteristic information, the disease grading information including rade information of a disease, a lesion type, a lesion location, and a number of lesions included by the disease; and constructing output information using the disease grading information. This embodiment improves the accuracy of grading information.

Abstract: The present disclosure discloses a method and apparatus for acquiring information. An implementation of the method comprises: acquiring a fundus image; introducing the fundus image into a pre-trained pathological classification model to obtain pathological classification information, wherein the pathological classification model is used for characterizing correspondence between a pathological area image contained from the fundus image and the pathological classification information, and the pathological classification information comprises at least one of diabetic retinopathy classification information and diabetic macular edema classification information; and establishing output information based on the pathological classification information.

Abstract: The present disclosure discloses a method and apparatus for analyzing medical image. A specific embodiment of the method includes: acquiring medical image data; generating multi-scale decision sample data based on the medical image data; inputting the multi-scale decision sample data into a deep neural network model to obtain an auxiliary diagnosis data of the medical image, the deep neural network model being trained according to a consistency principle between multi-scale training sample data and an output result of the deep neural network model. In the embodiment, a multi-scale training sample is used to accelerate the training process of the deep neural network model, thus the auxiliary diagnosis decision process can be accelerated, while the accuracy of the trained deep neural network model of the embodiment is improved according to a consistency principle of data between different scales and output results, thereby improving the accuracy of auxiliary diagnosis decision.

Abstract: An embodiment of the present disclosure discloses a method and apparatus for annotating a medical image. An embodiment of the method comprises: acquiring a to-be-annotated medical image; annotating classification information for the to-be-annotated medical image, wherein the classification information comprises a category of a diagnosis result and a grade of the diagnosis result corresponding to the medical image; processing the to-be-annotated medical image using a pre-trained lesion area detection model, framing a lesion area in the to-be-annotated medical image, and annotating a lesion type of the lesion area, to enable the to-be-annotated medical image to be annotated with the lesion area and the lesion type of the lesion area; and splitting the framed lesion area from the to-be-annotated medical image with the framed lesion area to form a split image of the to-be-annotated medical image, to enable the to-be-annotated medical image to be annotated with the split image.

Abstract: Systems and methods for image retrieval include constructing a plurality of graphs including a first graph for candidate images retrieved based upon holistic features of a query image and a second graph for candidate images retrieved based upon local features of the query image, wherein constructing includes weighting connected images based upon a Jaccard similarity coefficient. The plurality of graphs are fused to provide a fused graph. Candidate images of the fused graph are ranked, using a processor, to provide retrieval results of the query image.