Owkin, Inc.

Owkin France Sas

Centre Leon Berard

Gilles Wainrib

Pierre Courtiol

Charles Maussion

Eric W Tramel

Marc Sanselme

Jean-Yves Blay

Charlie Saillard

Benoit Schmauch

Thomas Clozel

Olivier Moindrot

Françoise Galateau Salle

Françoise Galateau Sallé

A method and apparatus of a device that classifies a mesothelioma image is described. In an exemplary embodiment, the device segments the mesothelioma 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.

Systems and methods for mesothelioma feature detection and enhanced prognosis or response to treatment

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the method includes tiling at least one region of interest of the input image into a set of tiles. For each tile, the method includes extracting a feature vector of the tile by applying a convolutional neural network, wherein a feature is a local descriptor of the tile; and computing a score of the tile from the extracted feature vector, said tile score being representative of a contribution of the tile into a classification of the input image. The method also includes sorting a set of the tile scores and selecting a subset of the tile scores based on their value and/or their rank in the sorted set. The method also includes applying a classifier to the selected tile scores in order to classify the input image.

Systems and methods for image classification

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.

Systems and methods for image preprocessing

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Pantin, France

Gilles Wainrib