Invention Grant
- Patent Title: Classifying medical images using deep convolution neural network (CNN) architecture
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Application No.: US15697454Application Date: 2017-09-07
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Publication No.: US10650286B2Publication Date: 2020-05-12
- Inventor: Rami Ben-Ari , Pavel Kisilev , Jeremias Sulam
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Gregory J. Kirsch
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G06N3/04 ; G06N3/08 ; G06K9/46 ; A61B5/055 ; A61B6/00 ; A61B5/00 ; G16H30/40 ; G16H50/70 ; G16H50/20

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.
Public/Granted literature
- US20190073569A1 CLASSIFYING MEDICAL IMAGES USING DEEP CONVOLUTION NEURAL NETWORK (CNN) ARCHITECTURE Public/Granted day:2019-03-07
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