Systems and methods for image preprocessing
A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.
1. A method of classifying an input image, the method comprising:
tiling a region of interest of the input image into a set of tiles;
extracting a first feature vector for the set of tiles by applying a first convolutional neural network, wherein the features of the feature vectors represent local descriptors of the region of interest;
extracting a second feature vector from the region of interest using a set of local labels generated for the input image;
combining the first and second feature vectors; and
processing the combined feature vectors to classify the input image.
2. The method of claim 1 , further comprising:
generating the set of local labels by applying a second convolution neural network to the set of tiles.
3. The method of claim 2 , wherein the second convolution neural network is a one-dimensional convolution neural network.
4. The method of claim 1 , wherein the classification is a global label for the digital image.
5. The method of claim 1 , wherein the extracting a first feature vector comprises:
extracting a feature vector for each of the tiles in the set of tiles.
6. The method of claim 5 , further comprising:
generating a local label for each of the tiles in the set of tiles.
7. The method of claim 1 , wherein the combining of the first and second feature vectors is a weighted pooling of the first feature vector and the second feature vector.
8. The method of claim 1 , wherein the combining of the first and second feature vectors is a concatenation of the first feature vector and the second feature vector.
9. The method of claim 1 , wherein the input image is a histopathology slide and the classification of the image is a diagnosis classification.
10. The method of claim 1 , wherein a classifier is used to classify the input image and the classifier is a multi-layer perceptron classifier, in particular comprising two fully connected layers.
11. A non-transitory computer readable medium with a memory storing code instructions which, when executed by a processor, cause the processor to perform operations for classifying an input image, the operations comprising:
tiling a region of interest of the input image into a set of tiles;
extracting a first feature vector for the set of tiles by applying a first convolutional neural network, wherein the features of the feature vectors represent local descriptors of the region of interest;
extracting a second feature vector from the region of interest using a set of local labels generated for the input image;
combining the first and second feature vectors; and
processing the combined feature vectors to classify the input image.
12. The non-transitory computer readable medium of claim 11 , further comprising:
generating the set of local labels by applying a second convolution neural network to the set of tiles.
13. The non-transitory computer readable medium of claim 12 , wherein the second convolution neural network is a one-dimensional convolution neural network.
14. The non-transitory computer readable medium of claim 11 , wherein the classification is a global label for the digital image.
15. The non-transitory computer readable medium of claim 11 , wherein the extracting a first feature vector comprises:
extracting a feature vector for each of the tiles in the set of tiles.
16. The non-transitory computer readable medium of claim 15 , further comprising:
generating a local label for each of the tiles in the set of tiles.
17. The non-transitory computer readable medium of claim 11 , wherein the combining of the first and second feature vectors is a weighted pooling of the first feature vector and the second feature vector.
18. The non-transitory computer readable medium of claim 11 , wherein the combining of the first and second feature vectors is a concatenation of the first feature vector and the second feature vector.
19. The non-transitory computer readable medium of claim 11 , wherein the input image is a histopathology slide and the classification of the image is a diagnosis classification.
20. The non-transitory computer readable medium of claim 11 , wherein a classifier is used to classify the input image and the classifier is a multi-layer perceptron classifier, in particular comprising two fully connected layers.