Abstract: The decision process of a first machine learning (ML) model may be explained based on a second ML model implemented on an apparatus. The apparatus may obtain a prediction about an image made based on the first ML model. The apparatus may further determine visual concepts associated with the image that may have been used by the first ML model to make the prediction, and determine respective contributions of the visual concepts to the prediction made by the first ML model. The apparatus may then generate, based on the second ML model, a textual description that explains the respective contributions of the visual concepts to the prediction made by the first ML model. The second ML model may determine respective image features associated with the visual concepts, map the determined image features to corresponding text features, and generate the textual description based at least on the text features.

Abstract: A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.

Abstract: An electrophoretic medium is disclosed, the electrophoretic medium comprising a plurality of a first type of particles, a plurality of a second type of particles, and a non-polar liquid. Each of the plurality of the first type of particles has a core and a shell. The core comprises a pigment. The shell of the first type of particles comprises a polymer, the polymer being a homopolymer or a copolymer. The homopolymer is formed by a vinylnaphthalene and the copolymer is formed by a vinylnaphthalene and a first monomer.

Abstract: An apparatus in accordance with embodiments of the present disclosure may obtain an image depicting one or more hands of a person in a medical environment; and detect, using a first machine learning (ML) model, a plurality of 2D landmarks associated with a hand of the person depicted in the image. The apparatus may further determine, using a second ML model, 3D features of the hand of the person based on the plurality of 2D landmarks. The apparatus may determine a gesture indicated by the hand of the person based on the 3D features of the hand predicted using the second ML model. Alternatively, in determining the 3D features of the hand, the system may stack the plurality of 2D landmarks across a sequence of image frames in a video, and use a third ML model to determine the 3D features of the hand based on the stacked 2D landmarks.

Abstract: An apparatus may obtain an image that depicts a first human body part and determine, based on the image, a classification label and a plurality of vertices associated with the first human body part. The classification label may indicate a class of the first human body part and the plurality of vertices may correspond to points of the first human body part in a three-dimensional (3D) space. The determinations may be made using an artificial neural network that includes a self-attention module and a graph convolution module. The apparatus may generate a first 3D model representative of the first human body part based at least on the plurality of vertices associated with the first human body part. The self-attention module may determine global features of the image indicating an interrelationship of the plurality of vertices and the graph convolution module may refine the global features determined by the self-attention module.

Abstract: Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.

Abstract: An apparatus for annotating a medical image may be configured to obtain, automatically, an outline of a region of interest (ROI) in the medical image and determine, based on one or more inner control points and one or more outer control points. The one or more inner control points may be located within the ROI and the one or more outer control points may be located outside of the ROI. The outline may be subsequently adjusted based on a user input and the adjusted outline may be used to generate a segmentation of the ROI.

Abstract: Described herein are machine learning (ML) based on systems, methods, and instrumentalities associated with image search and/or retrieval. An apparatus as described herein may obtain a query image and a textual description associated with the query image, and generate, using an artificial neural network (ANN), a feature representation that may represent the image and the textual description as an associated pair. Based on the feature representation, the apparatus may identify one or more images from an image repository and provide an indication regarding the one or more identified images, for example, as a ranked list.

Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

Abstract: An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.

Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.

Abstract: A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.

Abstract: A color electrophoretic display with improved black optical state includes an electrophoretic medium having five types of charged electrophoretic pigment particles in a non-polar fluid: a first particle having a first optical property and first charge polarity; a second particle having a second optical property and second opposite charge polarity with a first charge magnitude; a third particle having a third optical property and second charge polarity with a second charge magnitude smaller than the first charge; a fourth particle having a fourth optical property and second charge polarity with a third charge magnitude smaller than the second charge; and a fifth particle having a fifth optical property and second charge polarity with a fourth charge magnitude greater than the first charge. The first and fifth particles are white and black, respectively, and the second, third, and fourth particles are each a different one of cyan, magenta, and yellow.

Abstract: Disclosed herein are systems, methods and instrumentalities associated with multi-view 3D human model estimation using machine learning (ML) based techniques. These techniques may use synthetically generated data to train an ML model that may be used to progressively regress a 3D human body model based on multi-view 2D images. The training data may be synthetically generated based on statistical distributions of human poses and human body shapes, as well as a statistical distribution of camera viewpoints. The progressive regression may be performed based on consensus features shared by the multi-view images and diversity features derived from at least one of the multi-view images. Consistency between the multi-view images may also be maintained during the regression process.

Abstract: The physical characteristics of one or more anatomical structures of a person may change in accordance with conditions surrounding the determination of such physical characteristics. Machine learning based techniques may be used to determine a template representation of the one or more anatomical structures that may indicate the physical characteristics of the one or more anatomical structures free of the impact imposed by changing conditions. The template representation may then be used to predict the physical characteristics of the one or more anatomical structures under a new set of conditions, without subjecting the person to additional medical scans.

Abstract: Improved formulations of electrophoretic media that can be incorporated into displays, front plane laminates, inverted front plane laminates, or color changing films. The formulations include a non-polar fluid, a plurality of first charged particles, and charge control agents (CCA) including a quaternary amine and an unsaturated polymeric tail comprising monomers of at least 10 carbon atoms in length. The formulations show improved switching speeds, as well as a larger dynamic range at low temperatures (i.e., below about 0° C.), where compared to state-of-the-art electrophoretic media.

Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Abstract: A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.

Abstract: Click based contour editing includes detecting a selection input with respect to an image presented on a graphical user interface; designating an area of the image corresponding to the selection input as a region of interest; detecting at least one other selection input on the graphical user interface with respect to the image; determining if the at least one other selection input is within the region of interest or outside of the region of interest; and if the at least one other selection input is within the region of interest, excluding the portion of the image corresponding to the other input; or if the other selection input is outside of the region of interest, including the portion of the image corresponding to an area of the image associated with the other selection input.

Abstract: Human model recovery may be realized utilizing pre-trained artificially neural networks. A first neural network may be trained to determine body keypoints of a person based on image(s) of a person. A second neural network may be trained to predict pose parameters associated with the person based on the body keypoints. A third neural network may be trained to predict shape parameters associated with the person based on depth image(s) of the person. A 3D human model may then be generated based on the pose and shape parameters respectively predicted by the second and third neural networks. The training of the second neural network may be conducted using synthetically generated body keypoints and the training of the third neural network may be conducted using normal maps. The pose and shape parameters predicted by the second and third neural networks may be further optimized through an iterative optimization process.