Abstract: A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.

Abstract: A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.

Abstract: A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.

Abstract: A score of a histopathological diagnosis of cancer is generated by loading an image patch of an image into a processing unit, determining how many pixels of the image patch belong to a first tissue, processing additional image patches cropped from the image to determine how many pixels of each image patch belong to the first tissue, computing the score and displaying it along with the image on a graphical user interface. The image patch is cropped from the image of a slice of tissue that has been immunohistochemically stained using a diagnostic antibody. The first tissue comprises tumor epithelial cells that are positively stained by the diagnostic antibody. Determining how many pixels belong to the first tissue is performed by processing the image patch using a convolutional neural network. The score of the histopathological diagnosis is computed based on the total number of pixels belonging to the first tissue.

Abstract: According to some embodiments, a system and method are provided to classify an anomaly. The method comprises receiving, from an anomaly detection system, time-series data that comprises one or more anomalies. The time-series data is grouped into a plurality of groups based on a scale range. For each group of the plurality of groups, statistical features are extracted from the time-series data. The extracted statistical features associated with the plurality of groups are combined and the one or more anomalies are classified based on the combined extracted statistical features.

Abstract: A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.

Abstract: A score of a histopathological diagnosis of cancer is generated by loading an image patch of an image into a processing unit, determining how many pixels of the image patch belong to a first tissue, processing additional image patches cropped from the image to determine how many pixels of each image patch belong to the first tissue, computing the score and displaying it along with the image on a graphical user interface. The image patch is cropped from the image of a slice of tissue that has been immunohistochemically stained using a diagnostic antibody. The first tissue comprises tumor epithelial cells that are positively stained by the diagnostic antibody. Determining how many pixels belong to the first tissue is performed by processing the image patch using a convolutional neural network. The score of the histopathological diagnosis is computed based on the total number of pixels belonging to the first tissue.

Abstract: According to some embodiments, a system and method are provided to receive a first plurality of data from a machine associated with a first time period. A normal operation of the machine is automatically determined based on the first plurality of data. A second plurality of data may be received from the machine associated with a second time period. An anomaly in the second plurality of data is determined.

Abstract: According to some embodiments, a system and method are provided to classify an anomaly. The method comprises receiving, from an anomaly detection system, time-series data that comprises one or more anomalies. The time-series data is grouped into a plurality of groups based on a scale range. For each group of the plurality of groups, statistical features are extracted from the time-series data. The extracted statistical features associated with the plurality of groups are combined and the one or more anomalies are classified based on the combined extracted statistical features.

Abstract: According to some embodiments, a system and method are provided to receive a first plurality of data from a machine associated with a first time period. A normal operation of the machine is automatically determined based on the first plurality of data. A second plurality of data may be received from the machine associated with a second time period. An anomaly in the second plurality of data is determined.

Abstract: The present embodiments relate to a system and method associated with anomaly classification. The method comprises receiving a plurality of time-series data from one or more sensors associated with a machine. The time-series data may be automatically passed through a convolutional neural network to determine reduced dimension data. An anomaly based on classifying the reduced dimension data may be automatically determined. In a case that the anomaly is an unknown anomaly, the determined anomaly may be labeled and the determined anomaly and its associated label may be stored in an anomaly training database.