Abstract: Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Abstract: Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.

Abstract: Computer-implemented methods and systems are provided for curating medical images for a medical image database. An example method for curating involves operating a processor to obtain a query medical image, assign the query medical image to an initial set of related images, and generate a similarity indicator for each image of the initial set. The similarity indicator is representative of a measure of similarity between image data of that image and the initial set. The processor is operable to define an intermediate set from the initial set based on the similarity indicators; and generate a relevancy indicator for each image of the intermediate set. The relevancy indicator is representative of a measure of the relevancy between relevance of the image and the intermediate set. The processor is operable to define a curated set of images from the intermediate set based on the relevancy indicators.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: Various systems, methods, and non-transitory computer readable mediums for diagnosing a query X-ray image are disclosed. Example embodiments relate to operating the system to apply an artificial neural network model to the query X-ray image to extract a query feature vector representative of image characteristics of the query X-ray image; compare the query feature vector with one or more annotated feature vectors associated with respective one or more annotated X-ray images stored in an annotated image database to determine a similarity level between the query X-ray image and each annotated image of the one or more annotated images; associate the query X-ray image with a set of annotated images based at least on the similarity level and a similarity threshold; and assign the query X-ray image with a disease classification based at least on the disease classification of one or more annotated images of the set of annotated images.

Abstract: Computer-implemented methods and systems are provided for curating medical images for a medical image database. An example method for curating involves operating a processor to obtain a query medical image, assign the query medical image to an initial set of related images, and generate a similarity indicator for each image of the initial set. The similarity indicator is representative of a measure of similarity between image data of that image and the initial set. The processor is operable to define an intermediate set from the initial set based on the similarity indicators; and generate a relevancy indicator for each image of the intermediate set. The relevancy indicator is representative of a measure of the relevancy between relevance of the image and the intermediate set. The processor is operable to define a curated set of images from the intermediate set based on the relevancy indicators.

Abstract: Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Abstract: Computer-implemented methods and systems are provided for curating medical images for a medical image database. An example method for curating involves operating a processor to obtain a query medical image, assign the query medical image to an initial set of related images, and generate a similarity indicator for each image of the initial set. The similarity indicator is representative of a measure of similarity between image data of that image and the initial set. The processor is operable to define an intermediate set from the initial set based on the similarity indicators; and generate a relevancy indicator for each image of the intermediate set. The relevancy indicator is representative of a measure of the relevancy between relevance of the image and the intermediate set. The processor is operable to define a curated set of images from the intermediate set based on the relevancy indicators.

Abstract: Computer-implemented methods and systems are provided for automatically identifying features of a cytology specimen. An example method can involve dividing a whole slide image of the cytology specimen into a plurality of image portions; applying a first neural network to each image portion to identify one or more relevant image portions; and applying a second neural network to a first relevant image portion and a second relevant image portion to generate a respective first and second cell data. Each relevant image portion can be an image portion containing a region of interest for the cytology specimen. The method can further involve comparing the first and second cell data to determine whether a similarity threshold is satisfied; and in response to determining the similarity threshold is not satisfied, continuing to apply the second neural network to a subsequent relevant image portion until the similarity threshold is satisfied.

Abstract: Computer-implemented methods and systems are provided for curating medical images for a medical image database. An example method for curating involves operating a processor to obtain a query medical image, assign the query medical image to an initial set of related images, and generate a similarity indicator for each image of the initial set. The similarity indicator is representative of a measure of similarity between image data of that image and the initial set. The processor is operable to define an intermediate set from the initial set based on the similarity indicators; and generate a relevancy indicator for each image of the intermediate set. The relevancy indicator is representative of a measure of the relevancy between relevance of the image and the intermediate set. The processor is operable to define a curated set of images from the intermediate set based on the relevancy indicators.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: Computer-implemented methods and systems are provided for curating medical images for a medical image database. An example method for curating involves operating a processor to obtain a query medical image, assign the query medical image to an initial set of related images, and generate a similarity indicator for each image of the initial set. The similarity indicator is representative of a measure of similarity between image data of that image and the initial set. The processor is operable to define an intermediate set from the initial set based on the similarity indicators; and generate a relevancy indicator for each image of the intermediate set. The relevancy indicator is representative of a measure of the relevancy between relevance of the image and the intermediate set. The processor is operable to define a curated set of images from the intermediate set based on the relevancy indicators.

Abstract: A computer-implemented method and system are provided for generating an encoded representation for one or more images. The method involves operating a processor to, for at least one image portion of a plurality of image portions of the image, receive a set of projection data representing an image intensity of the image portion along a plurality of projection directions; and identify a subset of projection data from the set of projection data associated with one or more dominant features. The set of projection data includes a subset of projection data for each projection direction of the plurality of projection directions. The processor is operable to generate the encoded representation based at least on a data variation within the subset of projection data for the at least one image portion.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: A computer-implemented method and system are provided for generating an encoded representation for one or more images. The method involves operating a processor to, for at least one image portion of a plurality of image portions of the image, receive a set of projection data representing an image intensity of the image portion along a plurality of projection directions; and identify a subset of projection data from the set of projection data associated with one or more dominant features. The set of projection data includes a subset of projection data for each projection direction of the plurality of projection directions. The processor is operable to generate the encoded representation based at least on a data variation within the subset of projection data for the at least one image portion.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.