Abstract: A driving controller includes a logo detector. The logo detector includes a histogram extractor which receives input image data and extracts a first histogram from logo area data of the input image data, a first histogram regenerator electrically connected to the histogram extractor and configured to receive the first histogram from the histogram extractor to generate a second histogram based on the first histogram and a logo map determiner electrically connected to the histogram extractor and the first histogram regenerator, and configure to select one of the first histogram and the second histogram to generate a first logo map. The driving controller is configured to compensate the logo area data of the input image data using the first logo map.

Abstract: Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.

Abstract: Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Abstract: Disclosed is a system and a method for adapting a report of nodules in computed tomography (CT) scan image. A CT scan image may be resampled into a plurality of slices. A plurality of region of interests may be identified on each slice using an image processing technique. Subsequently, a plurality of nodules may be detected in each region of interest using the deep learning. Further, a plurality of characteristics associated with each nodule may be identified. The plurality of nodules may be classified into AI-confirmed nodules and AI-probable nodules based on a malignancy score. Further, feedback associated with the AI-confirmed nodules and the AI-probable may be received form a radiologist. Furthermore, data may be adapted based on the feedback. Finally, a report comprising adapted data may be generated.

Abstract: A computer-implemented method of facilitating neural network image analysis involves receiving signals representing a set of images, causing at least one neural network function to be applied to the set of images to determine at least one property confidence distribution parameter, and causing a cumulative distribution function defined at least in part by the at least one property confidence distribution parameter to be applied to a plurality of ranges, each range associated with a respective property that may be associated with the set of images, to determine a plurality of property confidences, each of the property confidences representing a confidence that the set of images should be associated with a respective one of the properties. Other methods, systems, and computer-readable media are disclosed.

Abstract: The present disclosure provides a method, device and non-transitory computer storage medium for processing an image. The method includes: obtaining an image by a monocular camera; extracting image features with different levels based on the image; determining a fused feature by fusing the image features with different levels; and determining a depth distribution feature map of the image based on the fused feature, where a pixel value of each pixel point in the depth distribution feature map is a depth value.

Abstract: Systems and methods for generating and tracking shapes of a target may be provided. The method may include obtaining at least one first resolution image corresponding to at least one of a sequence of time frames of a medical scan. The method may include determining, according to a predictive model, one or more shape parameters regarding a shape of a target from the at least one first resolution image. The method may include determining, based on the one or more shape parameters and a shape model, at least one shape of the target from the at least one first resolution image. The method may further include generating a second resolution visual representation of the target by rendering the determined shape of the target.

Abstract: Techniques for synthetic training data generation for machine learning are described. A user provides a synthetic training data generator with first set of images and a corresponding set of class identifiers each indicating a type of object depicted in a corresponding image. Each image depicts an object with a substantially or completely monochromatic or transparent background. A user also provides or identifies a second set of images to be used as backgrounds. The synthetic training data generator generates a plurality of images by overlaying one or more of the objects depicted in the first set of images over ones of the second set of images, and further generates labels for each of the plurality of images. The plurality of images and labels are used to automatically train and deploy a machine learning (ML) model.

Abstract: A vehicular driver monitoring system includes an illumination source that emits non-visible light that illuminates at least a portion of a driver of the vehicle, a reflector disposed at the vehicle and within a line of sight of the illuminated portion of the driver, a camera disposed in the vehicle and having a field of view that encompasses the reflector, and a control having an image processor that processes image data captured by the camera. The reflector reflects at least some non-visible light and allows visible light to pass through. The camera captures image data representative of the non-visible light emitted by the illumination source that reflects off the illuminated portion of the driver of the vehicle and reflects off the reflector so as to be directed toward the camera. The control, responsive to processing of image data captured by the camera, monitors the illuminated portion of the driver.

Abstract: The present invention provides a new image processing device that is robust against a change in capturing conditions. An image processing device 100 has an acquisition unit 110 for acquiring an image, a smoothing unit 120 for smoothing the acquired image with a prescribed smoothing level, a binarization unit 130 for binarizing the smoothed image, and a control unit 140 for making the smoothing unit 120 execute multiple sessions of smoothing differing in the smoothing level.

Abstract: According to an embodiment, a reading system includes a first extractor and a reader. The first extractor extracts, from an image in which a meter is imaged, a first region surrounded with a first contour, and a second region surrounded with a second contour positioned outward of the first contour. The reader calculates a first indication based on the first region, calculates a second indication based on the second region, calculates a first score relating to the first indication based on the first region, and calculates a second score relating to the second indication based on the second region.

Abstract: Embodiment herein discloses methods and devices for waste management by using an artificial intelligence based waste object categorizing engine. The method includes acquiring at least one image and detecting at least one waste object from the at least one acquired image. Additionally, the method determines that the at least one detected waste object matches with a pre-stored waste object and identifies a type of the detected waste object using the pre-stored waste object. Furthermore, the method includes displaying the type of the detected waste object based on the identification.

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

Abstract: System and methods are provided for producing computed tomography (CT) images. In some aspects, a method includes obtaining medical image data sets acquired using the multiple energies of irradiating radiation and analyzing the medical image data sets for spatial and spectral features. The method also includes comparing the spatial and spectral features of the medical image data sets to identify similarities and using the similarities, weighting the medical image data sets to generate images of the subject having reduced noise compared to images of the subject produced from the medical image data sets without weighting.

Abstract: This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.

Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

Abstract: A system and method are provided for optimizing histogram cumulative distribution function curves. In use, a first cumulative distribution function for a first histogram of a first pixel region of a first image is computed, and a first set of parameters for the first cumulative distribution function is extracted. A second cumulative distribution function for a second histogram of a second pixel region of the first image is computed, and a second set of parameters for the second cumulative distribution function is extracted. An interpolated cumulative distribution function comprising interpolated parameters calculated by interpolating between the first set of parameters and the second set of parameters is created. Additionally, a first equalized pixel in a second image based on a first input pixel in the first image and the interpolated cumulative distribution function is generated.

Abstract: A method of generating landmark locations for an image crop comprises: processing the crop through an encoder-decoder to provide a plurality of N output maps of comparable spatial resolution to the crop, each output map corresponding to a respective landmark of an object appearing in the image crop; processing an output map from the encoder through a plurality of feed forward layers to provide a feature vector comprising N elements, each element including an (x,y) location for a respective landmark. Any landmarks locations from the feature vector having an x or a y location outside a range for a respective row or column of the crop are selected for a final set of landmark locations; with remaining landmark locations tending to be selected from the N (x,y) landmark locations from the plurality of N output maps.

Abstract: An information processing device and method for sharing of compressed training data for neural network training is provided. The information processing device receives a first image which includes an object of interest. The information processing device extracts, from the received first image, a region of interest which includes the object of interest. Once extracted, the extracted region of interest is provided to an input layer of N numbers of layers of a first neural network, trained on an object detection task. The information processing device selects an intermediate layer of the first neural network and extracts a first intermediate result as an output generated by the selected intermediate layer of the first neural network based on the input RoI. Once extracted, the information processing device shares the extracted first intermediate result as compressed training data with a server to train a second neural network on the object detection task.

Abstract: An apparatus including a first memory area and a second memory area is provided with a control method including specifying one or a plurality of types of image processing to be applied in the first memory area, specifying a size of obtainment target image data as a predetermined size based on a memory capacity of the second memory area and a content of the specified one or plurality of types of image processing; and obtaining first divided image data having the specified predetermined size in predetermined image data.