Abstract: A computer implemented method and a machine learning model for predicting the likelihood that a subject having breast cancer will experience a relapse following treatment, predicting a tumor within a whole slide image, and/or predicting a status of a biomarker in breast cancer tissue is provided.

Abstract: 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.

Abstract: A computer implemented method and a machine learning model for predicting the likelihood that a subject having breast cancer will experience a relapse following treatment, predicting a tumor within a whole slide image, and/or predicting a status of a biomarker in breast cancer tissue is provided.

Abstract: 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.

Abstract: A method of modelling for checking the results provided by an artificial neural network, includes generating an artificial neural network; training the artificial neural network on a training database; testing the artificial neural network on at least one test datum dependent on a plurality of variables vi; so as to obtain a result R per test datum, the result R being dependent on the variables vi; for each result R: approximating by a linear model a first function F1 dependent solely on the result R so as to obtain a second function F2, the first function F1 and the second function F2 being dependent on the variables vi; simplifying the second function F2 to obtain a third function F3 dependent on a smaller number of variables vi; applying to the third function F3 the inverse function of the first function F1 to obtain an operating model of the neural network.

Abstract: 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.