Abstract: A method for depth mapping of objects in a scene by a system of a moving/movable platform is disclosed. According to some embodiments, the method includes actively illuminating the scene with pulsed light that is generated by at least one pulsed light illuminator and receiving, responsive to illuminating the scene, reflections on at least one image sensor that comprises a plurality of pixel elements. The method may further include gating at least one of the plurality of pixel elements of the at least one image sensor for converting the reflections into pixel values for generating reflection-based images that have at least two depth-of-field ranges and an overlapping DOF region. In addition, the method may include determining, based on at least one first pixel value of a first DOF in the overlapping DOF region, and based on at least one second pixel value of a second DOF in the overlapping DOF region, depth information of one or more objects located in the overlapping DOF region of the scene.

Abstract: Methods, systems, and devices for wireless communications are described. The method may include a user equipment (UE) transmitting, to a base station, signaling indicating a capability of the UE to utilize a demodulation reference signal (DMRS) for channel state information (CSI) reporting. Upon receiving the signaling, the base station may transmit the DMRS and the UE may update a parameter of a CSI report in response to the received DMRS. The UE may then transmit the CSI report to the base station and the base station may utilize the CSI report for subsequent communications with the UE.

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 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 computer implemented method for localizing target anatomical regions of interest (ROI) of a target individual, comprising: uniformly sub-sampling a plurality of 2D images having sequential index numbers within a 3D anatomical volume, feeding the plurality of sampled 2D images into a classifier for outputting a plurality of values on a normalized anatomical scale, fitting a linear model to the plurality of values and corresponding sequential index numbers, mapping by the linear model, the plurality of 2D images to the normalized anatomical scale, receiving an indication of at least one target anatomical ROI of a target individual, wherein each target anatomical ROI is mapped to the normalized anatomical scale, and providing a sub-set of the plurality of 2D images having values of the normalized anatomical scale corresponding to the received at least one target anatomical ROI.

Abstract: There is provided a method of training a neural network for reconstructing of a 3D point cloud from 2D image(s), comprising: extracting point clouds each represented by an ordered list of coordinates, from 3D anatomical images depicting a target anatomical structure, selecting one of the plurality of point clouds as a template, non-rigidly registering the template with each of the point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the warped templates are consistent in terms of coordinate order, receiving 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images, and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping 2D anatomical image(s) into a 3D point cloud.

Abstract: There is provided a method of training a neural network for reconstructing of a 3D point cloud from 2D image(s), comprising: extracting point clouds each represented by an ordered list of coordinates, from 3D anatomical images depicting a target anatomical structure, selecting one of the plurality of point clouds as a template, non-rigidly registering the template with each of the point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the warped templates are consistent in terms of coordinate order, receiving 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images, and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping 2D anatomical image(s) into a 3D point cloud.

Abstract: There is provided a computer implemented method for localizing target anatomical regions of interest (ROI) of a target individual, comprising: uniformly sub-sampling a plurality of 2D images having sequential index numbers within a 3D anatomical volume, feeding the plurality of sampled 2D images into a classifier for outputting a plurality of values on a normalized anatomical scale, fitting a linear model to the plurality of values and corresponding sequential index numbers, mapping by the linear model, the plurality of 2D images to the normalized anatomical scale, receiving an indication of at least one target anatomical ROI of a target individual, wherein each target anatomical ROI is mapped to the normalized anatomical scale, and providing a sub-set of the plurality of 2D images having values of the normalized anatomical scale corresponding to the received at least one target anatomical ROI.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: A method for depth mapping of objects in a scene by a system of a moving/movable platform is disclosed, comprising actively illuminating the scene with pulsed light that is generated by at least one pulsed light illuminator; receiving, responsive to illuminating the scene, reflections on at least one image sensor that comprises a plurality of pixel elements; gating at least one of the plurality of pixel elements of the at least one image sensor for converting the reflections into pixel values for generating reflection-based images that have at least two depth-of-field ranges and an overlapping DOF region; and determining, based on at least one first pixel value of a first DOF in the overlapping DOF region, and based on at least one second pixel value of a second DOF in the overlapping DOF region, depth information of one or more objects located in the overlapping DOF region of the scene.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.