Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

Abstract: Embodiments may provide methods by which fusion of information may be applied to uncertainty reduction in the results of classifier-based data analysis. For example, a method for data analysis may comprise generating a plurality of sets of data samples, each set of data samples representing a portion of input data at plurality of scales, each data sample in a set may represent the portion of the input data at a different scale and location, generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, each data sample may be processed with multi-layer convolutional processing separately from other data samples, and generating a feature map by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing.

Abstract: A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

Abstract: A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.

Abstract: A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

Abstract: A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

Abstract: A computer-implemented method includes receiving multimodal data. The computer-implemented method further includes generating one or more kernel matrices from the multimodal data. The computer-implemented method further includes generating an equivalent kernel matrix using one or more coefficient matrices, wherein the one or more coefficient matrices are constrained by a nuclear norm. The computer-implemented method further includes initiating one or more iterative processes. Each of the one or more iterative processes includes: calculating an error for the one or more coefficient matrices of the equivalent kernel matrix based on a training set, and initiating a line search for the one or more coefficient matrices of the equivalent kernel matrix. The computer-implemented method further includes, responsive to generating an optimal coefficient matrix, terminating the one or more iterative processes. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A method comprising using at least one hardware processor for applying a mapping function to a medical image, to generate a semantic description of a visual finding in the medical image. The mapping function is optionally an MRF (Markov random field)-based, SVM (Support Vector Machine) mapping function.

Abstract: Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.

Abstract: A computer-implemented method, computerized apparatus and computer program product for providing performance feedback to physicians. An automatic diagnostic tool is applied to a benchmark of cases of a physician to diagnose whether a predetermined medical procedure is required. A discrepancy relation is determined by comparing the percentage of cases in the benchmark the tool diagnosed as requiring the procedure with an expected percentage determined based on the percentage of cases the physician diagnosed as requiring the procedure and the tool's accuracy. A feedback is provided to the physician responsive to a discrepancy indicated by the discrepancy relation.

Abstract: A computer-implemented method, computerized apparatus and computer program product for detecting fraud based on assessment of phyisicians' activity. An automatic diagnostic tool is applied to a benchmark of cases of a physician to diagnose whether a predetermined procedure is required. A discrepancy relation is determined by comparing the percentage of cases in the benchmark the tool diagnosed as requiring the procedure with an expected percentage determined based on the percentage of cases diagnosed by the physician as requiring the procedure and the tool's accuracy. An alert is provided to a supervising entity responsive to a discrepancy indicated by the discrepancy relation.

Abstract: Methods, computing systems and computer program products implement embodiments of the present invention that include defining a first number of categories, each of the categories having a respective second number of values, and assigning, to each given examination question in a set of examination questions, a respective third number of categories and at least one given value for each of the third number of categories. A a set of requirements is retrieved, each given requirement including a respective fourth number of category requirements and a respective value-requirement for each of the category requirements, and a test-suite minimization algorithm is executed in order to select a minimum subset of the examination questions having categories and respective values including the category requirements and the value-requirements.

Abstract: Detecting regions-of-interest in medical images by identifying one or more image features in one or more medical images of a subject patient, identifying one or more clinical descriptors within clinical records of the subject patient, and identifying, using a visual-textual relationship model, regions-of-interest within the medical images of the subject patient based on relationships within the visual-textual relationship model corresponding to relationships between the image features identified in the subject patient medical images and the clinical descriptors identified in the subject patient clinical records.

Abstract: An image is processed in a device-independent color space by applying a modification function to one or more dimensions of that color space. The image is then converted into a target device-dependent color space. In order to minimize unwanted changes of color arising from the modification function taking image color values out of gamut when converted to the target device-dependent color space, the modification function is scaled to limit modified image values to within modification limits that are determined, for each image pixel or group of pixels, in dependence on the target device-dependent color space gamut boundary in the device-independent color space.

Abstract: A computer implemented method, a computerized system and a computer program product for image representation set creation. The computer implemented method comprises obtaining an image of a subject, wherein the image is produced using an imaging modality. The method further comprises automatically determining, by a processor, values to an image representation set with respect to the image, wherein the image representation set consists of semantic representation parameters of the image according to the imaging modality and according to a clinical diagnosis problem that is ascertainable from the image, wherein a total number of combinations of values of the semantic representation parameters is below a human comprehension threshold; and determining a decision regarding the clinical diagnosis problem based on the values of the image representation set of the image.

Abstract: In one implementation, a plurality of signature vectors from a multi-dimensional representation of a graphical object is generated. Each of the signature vectors comprises attributes that vary little in response to changes in shape, size, orientation, and visual layer appearance of the graphical object, and each of the signature vectors includes attributes based on operations of integration, differentiation, and transforms on the multi-dimensional representation of the graphical object. Each signature vector is composited into multiple portions from the plurality of signature vectors to define an appearance-invariant signature of the graphical object.

Abstract: A computer implemented method, a computerized system and a computer program product for automatic image segmentation. The computer implemented method comprises obtaining an image of a tissue, wherein the image is produced using an imaging modality. The method further comprises automatically identifying, by a processor, a tissue segment within the image, wherein said identifying comprises identifying an artifact within the image, wherein the artifact is a misrepresentation of a tissue structure, wherein the misrepresentation is associated with the imaging modality; and searching for the tissue segment in a location adjacent to the artifact.

Abstract: A computer implemented method, a computerized system and a computer program product for generating questions. The computer implemented method comprising obtaining a question, wherein the question comprises one or more elements that define an answer for the question. The method further comprising obtaining the answer. The method further comprises automatically generating, by a processor, a new question based on the question and the answer. The automatic generation comprises determining a variant of the one or more elements, wherein the variant defines the answer, wherein the new question comprises the variant.

Abstract: In one implementation, a graphical object classification system includes an acquisition module, a signature generation module, and a classification module. The acquisition module accesses a representation of a graphical object. The signature generation module generates an appearance-invariant signature of the graphical object based on the representation. The classification module classifies the graphical object based on the appearance-invariant signature.

Abstract: A method comprising using at least one hardware processor for applying a mapping function to a medical image, to generate a semantic description of a visual finding in the medical image. The mapping function is optionally an MRF (Markov random field)-based, SVM (Support Vector Machine) mapping function.