Abstract: Methods and systems for clinical report generation. One system includes an electronic processor configured to receive a query image and determine a similarity metric for a plurality of medical images, where the similarity metric represents a similarity between the query image and each of the plurality of medical images. The electronic processor is also configured to determine a predetermined number of medical images from the plurality of medical images based on the similarity metric for each of the plurality of medical images. The electronic processor is also configured to rank a plurality of reports, where each of the plurality of reports correspond to one of the predetermined number of medical images. The electronic processor is also configured to generate a clinical report including information extracted from at least one of the plurality of reports based on the ranking of the plurality of reports.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement an automated medical imaging report generator which receives an input medical image and inputs the input medical image into a machine learning (ML) computer model trained to predict finding labels based on patterns of image features extracted from the medical image. The ML computer model generates a prediction of a finding label applicable to the input medical image in terms of a finding label prediction output vector. Based on the finding label prediction output vector, a lookup operation is performed, in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label. An output medical imaging report is generated for the input medical image based on natural language content of the matching medical imaging report data structure.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Methods and systems for clinical report generation. One system includes an electronic processor configured to receive a query image and determine a similarity metric for a plurality of medical images, where the similarity metric represents a similarity between the query image and each of the plurality of medical images. The electronic processor is also configured to determine a predetermined number of medical images from the plurality of medical images based on the similarity metric for each of the plurality of medical images. The electronic processor is also configured to rank a plurality of reports, where each of the plurality of reports correspond to one of the predetermined number of medical images. The electronic processor is also configured to generate a clinical report including information extracted from at least one of the plurality of reports based on the ranking of the plurality of reports.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: A handheld apparatus for contactless acquisition of a fingerprint image is disclosed. One such apparatus includes an enclosure and at least three digital image capture devices housed within the enclosure and arranged along an arc. The enclosure includes a hollow finger receiving area, sized and shaped to receive a finger. Each of the digital image capture devices is positioned along the arc at substantially a same distance from the finger receiving area and positioned along the arc to have a respective optical axis that lies at about 45° to another of the digital image capture devices. The optical axes intersect within the enclosure. Each digital image capture device is operable to acquire a partial fingerprint image of the finger in a contactless manner. Each partial fingerprint image corresponds to a different side of the finger. The acquired fingerprint images combine to produce a nail-to-nail fingerprint image.

Abstract: Methods and/or systems for scanning biometric objects and compiling 2-dimensional digital images thereof, such images as can provide enhanced resolution, with reduced distortion.

Abstract: In accordance with an embodiment of the invention, there is provided an apparatus for characterizing and identifying a human. The apparatus comprises a light imaging device that images topography of a surface of a portion of human anatomy, and an infrared imaging device that images infrared radiation of the same portion of human anatomy. The light imaging device and the infrared imaging device are rotatable about at least one axis, each of the at least one axis extending through the portion of the anatomy.

Abstract: Methods and/or systems for scanning biometric objects and compiling 2-dimensional digital images thereof, such images as can provide enhanced resolution, with reduced distortion.