Abstract: There is provided a method for generating a composite 2D interpretation image, comprising inputting each 2D medical image of a divided 3D image, into a 2D classifier trained on 2D images labelled with an indication of a visual finding, computing a respective explanation map for each respective 2D image, the respective explanation map including regions corresponding to corresponding regions of the respective 2D image, each respective region of the respective explanation map is associated with a computed explainable weight indicative of an influence of the respective corresponding region of the respective 2D image on the outcome of the 2D classifier fed the respective 2D image, and computing a composite 2D interpretation image comprising a respective aggregation weight for each respective region thereof, each respective aggregation weight computed by aggregating the explainable weights computed for the respective regions corresponding to the respective region of the composite 2D interpretation image.

Abstract: There is provided a method for generating a composite 2D interpretation image, comprising inputting each 2D medical image of a divided 3D image, into a 2D classifier trained on 2D images labelled with an indication of a visual finding, computing a respective explanation map for each respective 2D image, the respective explanation map including regions corresponding to corresponding regions of the respective 2D image, each respective region of the respective explanation map is associated with a computed explainable weight indicative of an influence of the respective corresponding region of the respective 2D image on the outcome of the 2D classifier fed the respective 2D image, and computing a composite 2D interpretation image comprising a respective aggregation weight for each respective region thereof, each respective aggregation weight computed by aggregating the explainable weights computed for the respective regions corresponding to the respective region of the composite 2D interpretation image.

Abstract: A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.

Abstract: A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.

Abstract: Embodiments of the present systems and methods may provide techniques for measuring similarity between two datasets using classification error as a measure of the similarity between the two datasets and for improving the similarity between the two datasets. For example, in an embodiment, a computer-implemented method for determining treatment effects may comprise receiving data relating to observations of treatments outcomes of at least one treatment in a plurality of treatment groups, wherein the data for each treatment group forms a dataset, reweighting at least some of the datasets to balance biases in the data among the datasets by: determining bias between at least two datasets using a classification error; and generating balancing weights for at least one of the datasets to reduce the bias between the at least two dataset, and determining treatment effects using at least one reweighted dataset.

Abstract: Characterizing subpopulations by their response to a given exposure relative to an alternative. Data for a set of subjects is received, including two exposures, two outcomes, and a set of characteristics. For a number of subsets an outcome model that estimates the probability of an outcome, given an exposure and the characteristics, is trained; an individual odds ratio (iOR), based on the outcome model, is computed; a sparse model that classifies subjects into a high-iOR group or another group is trained; the characteristics used in the sparse model are recorded; and, based on the recorded characteristics, a primary set of characteristics is selected. Another outcome model, based on the set of subjects and the characteristics, is trained. An iOR based on the other outcome model is computed. A sparse model for classifying subjects into a high-iOR group or another group, using the primary set of characteristics, is trained.

Abstract: A computer-implemented method and apparatus for supporting decisions in sequential clinical risk assessment examinations, the method comprising receiving one or more first test results and a question, both associated with a patient; and assessing by a processor associated with a computing platform, information gain provided by a second test which may be performed for the patient, as the conditional mutual information between a second test and the question, using the first test result.

Abstract: A computer-implemented method and apparatus for supporting decisions in sequential clinical risk assessment examinations, the method comprising receiving one or more first test results and a question, both associated with a patient; and assessing by a processor associated with a computing platform, information gain provided by a second test which may be performed for the patient, as the conditional mutual information between a second test and the question, using the first test result.

Abstract: A speaker adaptation system and method for speech models of symbols displays a multi-word symbol to be spoken as a symbol. The supervised adaptation system and method has unsupervised adaptation for multi-word symbols, limited to the set of words associated with each multi-word symbol.

Abstract: A method of recognizing speech includes determining active ranges of states to be processed for each frame and performing recognition operations for each frame only on states within the active ranges.

Abstract: A speaker adaptation system and method for speech models of symbols displays a multi-word symbol to be spoken as a symbol. In another embodiment of the invention, the adaptation system and method has unsupervised adaptation for multi-word symbols, limited to the set of words associated with each multi-word symbol.