Invention Grant
- Patent Title: Disease detection from weakly annotated volumetric medical images using convolutional long short-term memory
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Application No.: US16600009Application Date: 2019-10-11
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Publication No.: US11195273B2Publication Date: 2021-12-07
- Inventor: Nathaniel Mason Braman , Ehsan Dehghan Marvast , David James Beymer
- 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
- Agency: Michael Best & Friedrich LLP
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06K9/66 ; G06K9/46 ; G06T11/00 ; G06N3/04

Abstract:
Systems and methods for developing a disease detection model. One method includes training the model using an image study and an associated disease label mined from a radiology report. The image study including a sequence of a plurality of two-dimensional slices of a three-dimensional image volume, and the model including a convolutional neural network layer and a convolutional long short-term memory layer. Training the model includes individually extracting a set of features from each of the plurality of two-dimensional slices using the convolutional neural network layer, sequentially processing the features extracted by the convolutional neural network layer for each of the plurality of two-dimensional slices using the convolutional long short-term memory layer, processing output from the convolutional long short-term memory layer for a sequentially last of the plurality of two-dimensional slices to generate a probability of the disease, and updating the model based on comparing the probability to the label.
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