Disease detection from weakly annotated volumetric medical images using convolutional long short-term memory
US11195273B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 11, 2019 |
| Grant date | Dec 7, 2021 |
| Priority date | — |
| Expiry date | Oct 11, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.