Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: A non-transitory, computer-readable medium includes instructions that causes at least one processing device to display a graphical user interface (GUI) configured to facilitate generating a synthetic dataset including a plurality of images. The GUI includes a dataset size selector to receive user input to indicate a number of images to generate and include in the synthetic dataset; a target object type selector to receive user input indicative of at least one selected target object type to feature in the synthetic dataset; one or more image parameter variability controls to receive user input indicative of at least one variation to include in the synthetic dataset relative to target object representations generated based on the at least one selected target object type; and a dataset generation control to initiate generating the synthetic dataset. The synthetic dataset is generated according to the size input, target object type input, and variability input.

Abstract: A non-transitory, computer-readable medium includes instructions that causes at least one processing device to display a graphical user interface (GUI) configured to facilitate generating a synthetic dataset including a plurality of images. The GUI includes a dataset size selector to receive user input to indicate a number of images to generate and include in the synthetic dataset; a target object type selector to receive user input indicative of at least one selected target object type to feature in the synthetic dataset; one or more image parameter variability controls to receive user input indicative of at least one variation to include in the synthetic dataset relative to target object representations generated based on the at least one selected target object type; and a dataset generation control to initiate generating the synthetic dataset. The synthetic dataset is generated according to the size input, target object type input, and variability input.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: There is provided a system for computing a single-label neural network for detection of an indication of an acute medical condition, comprising: hardware processor(s) executing a code for: providing a multi-label training dataset including anatomical images each associated with a label indicative of visual finding type(s), or indicative of no visual finding types, training a multi-label neural network for detection of the visual finding types(s) in a target anatomical image according to the multi-label training dataset, creating a single-label training dataset including anatomical images each associated with a label indicative of the selected single visual finding type, or indicative of an absence of the single visual finding type, and training a single-label neural network for detection of the single visual finding type, by setting the trained multi-label neural network as an initial baseline of the single-label neural network, and fine-tuning and/or re-training the baseline according to the single-label tra

Abstract: A system for prioritizing patients for treatment, comprising: at least one hardware processor executing a code for: feeding anatomical images into a visual filter neural network for outputting a category indicative of a target body region depicted at a target sensor orientation and a rotation relative to a baseline, rejecting a sub-set of anatomical images classified into another category, rotating to the baseline images classified as rotated, identifying pixels for each image having outlier pixel intensity values denoting an injection of content, adjusting the outlier pixel intensity values to values computed as a function of non-outlier pixel intensity values, feeding each the remaining sub-set of images with adjusted outlier pixel intensity values into a classification neural network for detecting the visual finding type, generating instructions for creating a triage list for which the classification neural network detected the indication, wherein patients are selected for treatment based on the triage lis

Abstract: There is provided a method comprising: providing two anatomical images of a target individual, each captured at a unique orientation of the target individual, inputting first and second anatomical images respectively into a first and second convolutional neural network (CNN) of a classifier to respectively output first and second feature vectors, inputting a concatenation of the first and second feature vectors into a fully connected layer of the classifier, and computing an indication of distinct visual finding(s) present in the anatomical images by the fully connected layer, wherein the statistical classifier is trained on a training dataset including two anatomical images of each respective sample individual, each image captured at a respective unique orientation of the target individual, and a tag created based on an analysis that maps respective individual sentences of a text based radiology report to one of multiple indications of visual findings.

Abstract: A system for prioritizing patients for treatment, comprising: at least one hardware processor executing a code for: feeding anatomical images into a visual filter neural network for outputting a category indicative of a target body region depicted at a target sensor orientation and a rotation relative to a baseline, rejecting a sub-set of anatomical images classified into another category, rotating to the baseline images classified as rotated, identifying pixels for each image having outlier pixel intensity values denoting an injection of content, adjusting the outlier pixel intensity values to values computed as a function of non-outlier pixel intensity values, feeding each the remaining sub-set of images with adjusted outlier pixel intensity values into a classification neural network for detecting the visual finding type, generating instructions for creating a triage list for which the classification neural network detected the indication, wherein patients are selected for treatment based on the triage lis

Abstract: There is provided a system for computing a single-label neural network for detection of an indication of an acute medical condition, comprising: hardware processor(s) executing a code for: providing a multi-label training dataset including anatomical images each associated with a label indicative of visual finding type(s), or indicative of no visual finding types, training a multi-label neural network for detection of the visual finding types(s) in a target anatomical image according to the multi-label training dataset, creating a single-label training dataset including anatomical images each associated with a label indicative of the selected single visual finding type, or indicative of an absence of the single visual finding type, and training a single-label neural network for detection of the single visual finding type, by setting the trained multi-label neural network as an initial baseline of the single-label neural network, and fine-tuning and/or re-training the baseline according to the single-label tra

Abstract: There is provided a method comprising: providing two anatomical images of a target individual, each captured at a unique orientation of the target individual, inputting first and second anatomical images respectively into a first and second convolutional neural network (CNN) of a classifier to respectively output first and second feature vectors, inputting a concatenation of the first and second feature vectors into a fully connected layer of the classifier, and computing an indication of distinct visual finding(s) present in the anatomical images by the fully connected layer, wherein the statistical classifier is trained on a training dataset including two anatomical images of each respective sample individual, each image captured at a respective unique orientation of the target individual, and a tag created based on an analysis that maps respective individual sentences of a text based radiology report to one of multiple indications of visual findings.

Abstract: A method and system for determining occupancy in a space, include determining presence of an occupant in a space based a signal from a PIR sensor monitoring the space and on analysis of an image of the space. Assigning different weights to the PIR signal and image analysis enables controlling a device in the space differently.

Abstract: A method and system for detecting occupancy in a space use computer vision techniques. In one embodiment an object is detected in an image of the space. If the object is detected in a first area of the image, a shape of the object is determined based on a first shape feature of the object and if the object is detected in a second area of the image, the shape of the object is determined based on a second shape feature of the object. The object may be determined to be an occupant based on the determined shape of the object.

Abstract: A method and system for detecting occupancy in a space use computer vision techniques. In one embodiment an object is detected in an image of the space. If the object is detected in a first area of the image, a shape of the object is determined based on a first shape feature of the object and if the object is detected in a second area of the image, the shape of the object is determined based on a second shape feature of the object. The object may be determined to be an occupant based on the determined shape of the object.

Abstract: A system and method are provided for determining a body position of an occupant form an image, based on the shape of the occupant and based on a visual surrounding of the shape of the occupant in the image.

Abstract: A method and system for determining a location of a sitting occupant in a space include detecting a sitting occupant in an image of the space and determining a location of the sitting occupant on a floor of the space in the image, based on a shape of the sitting occupant in the image. The location on the floor in the image can be transformed to a real world location.

Abstract: A method and system for determining occupancy in a space, include determining presence of an occupant in a space based a signal from a PIR sensor monitoring the space and on analysis of an image of the space. Assigning different weights to the PIR signal and image analysis enables controlling a device in the space differently.

Abstract: A method and system for determining a location of an occupant in a space include detecting a shape of an occupant in an image of a space; determining a location of the occupant on a floor of the space in the image, based on the shape of the occupant; and transforming the location on the floor in the image to a location of the occupant on the floor in the space.

Abstract: A method and system are provided for automatically determining occupancy in a space by obtaining rotation invariant data from at least one image from a sequence of images of the space; detecting a shape of an occupant in the at least one image based on the rotation invariant data; and determining occupancy based on the detection of the shape of the occupant.