CLASSIFYING MEDICAL IMAGES USING DEEP CONVOLUTION NEURAL NETWORK (CNN) ARCHITECTURE
First Claim
1. A computer-implemented method for classifying medical images with a global image-level tag and without local annotations that reveal a position and a shape of findings within the image, comprising:
- receiving a plurality of image tiles from a whole image, each image tile including a portion of the whole image, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume; and
classifying the whole image by a trained model based on the generated three-dimensional feature volume to form a classification of the image.
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Accused Products
Abstract
Embodiments of the present systems and methods may provide the capability to classify medical images, such as mammograms, in an automated manner using existing annotation information. In embodiments, only the global, image level tag may be needed to classify a mammogram into certain types, without fine annotation of the findings in the image. In an embodiment, a computer-implemented method for classifying medical images may comprise receiving a plurality of image tiles, each image tile including a portion of a whole view, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume and classifying the larger image by a trained model based on the generated three-dimensional feature volume to form a classification of the image.
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Citations
20 Claims
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1. A computer-implemented method for classifying medical images with a global image-level tag and without local annotations that reveal a position and a shape of findings within the image, comprising:
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receiving a plurality of image tiles from a whole image, each image tile including a portion of the whole image, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume; and classifying the whole image by a trained model based on the generated three-dimensional feature volume to form a classification of the image. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for classifying medical images with a global image-level tag and without local annotations that reveal a position and a shape of findings within the image, the system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:
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receiving a plurality of image tiles from a whole image, each image tile including a portion of the whole image, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume; and classifying the whole image by a trained model based on the generated three-dimensional feature volume to form a classification of the image. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer program product for classifying medical images with a global image-level tag and without local annotations that reveal a position and a shape of findings within the image, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
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receiving a plurality of image tiles from a whole image, each image tile including a portion of the whole image, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume; and classifying the whole image by a trained model based on the generated three-dimensional feature volume to form a classification of the image. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification