Abstract: An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data.

Abstract: A detection and communication module arranged to implement a face detection function and an optical wireless communication function, and including a processing unit, a transmission chain and a reception chain, the processing unit being arranged to transmit via the transmission chain a detection signal, to receive via the reception chain the detection signal following its reflection on a face surface of an individual, and to evaluate a distance between the detection and communication module and the face surface of the individual, the processing unit being further arranged to transmit via the transmission chain a transmitted optical wireless communication signal containing data to be transmitted, and to receive via the reception chain a received optical wireless communication signal.

Abstract: Disclosed herein is a system and method for improving the accuracy of an object detector when trained with a dataset having a significant number of missing annotations. The method uses a novel Background Recalibration Loss (BRL) which adjusts the gradient direction according to its own activation to reduce the adverse effect of error signals by replacing the negative branch of the focal loss with a mirror of the positive branch when the activation is below a confusion threshold.

Abstract: Herein disclosed are robotic dentistry methods comprising tooth extraction and crown preparation. Robotic tooth extraction may comprise determining if the correct tooth is targeted, based on a scanned image; determining a decay state of the tooth based on the scanned image; selecting an extraction technique determined as a function of the decay state; and extracting the tooth using the selected extraction technique. Robotic crown preparation may comprise receiving an initial reduction plan to reduce a tooth; presenting, to a dentist for approval, the initial reduction plan for approval; upon approval, reducing the tooth according to the approved initial reduction plan; upon rejection, modifying the initial reduction plan based on dentist input; receiving the modified reduction plan to reduce the tooth; presenting, to a dentist for approval, the modified reduction plan for approval; and upon approval of the modified reduction plan, reducing the tooth according to the approved modified reduction plan.

Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

Abstract: An apparatus (1), for use in conjunction with a medical imaging device (2) having an imaging device controller (4) that displays a graphical user interface (GUI) (8) including a preview image viewport (9), includes at least one electronic processor (20) programmed to: perform an image analysis (38) on a preview image displayed in the preview image viewport to generate preview-derived image label information; extract GUI-derived image label information from the GUI excluding the preview image displayed in the preview image viewport; and output an alert (30) when the preview-derived image label information and the GUI-derived image label information are not consistent.

Abstract: According to one embodiment, an anomaly detection device includes a processor that is configured to acquire input data. The processor derives a first anomaly degree corresponding to a difference between first feature data derived from the input data using a trained deep model and second feature data derived from the input data using a trained prediction model. The processor derives a second anomaly degree corresponding to an estimated relative positional relationship between a first and second region in the image data based on the second feature data. A total anomaly degree for the input data is then calculated from the first anomaly degree and the second anomaly degree.

Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Abstract: The technology disclosed introduces two types of neural networks: “master” or “generalists” networks and “expert” or “specialists” networks. Both, master networks and expert networks, are fully connected neural networks that take a feature vector of an input hand image and produce a prediction of the hand pose. Master networks and expert networks differ from each other based on the data on which they are trained. In particular, master networks are trained on the entire data set. In contrast, expert networks are trained only on a subset of the entire dataset. In regards to the hand poses, master networks are trained on the input image data representing all available hand poses comprising the training data (including both real and simulated hand images).

Abstract: Various implementations disclosed herein include devices, systems, and methods that determine a wrist measurement or watch band size using depth data captured by a depth sensor from one or more rotational orientations of the wrist. In some implementations, depth data captured by a depth sensor including at least two depth map images of a wrist from different angles is obtained. In some implementations, an output is generated based on inputting the depth data into a machine learning model, the output corresponding to circumference of the wrist or a watch band size of the wrist. Then, a watch band size recommendation is provided based on the output.

Abstract: Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the tar

Abstract: A method and a system for planning an orthodontic treatment are provided. The method comprises: acquiring an arch form 3D digital model including a representation of the given tooth in a current position thereof within a subject's gingiva; determining an initial crown reference point and an initial root reference point; obtaining a target position of the given tooth within the arch form 3D digital model; determining a number of steps for the given tooth to displace from the current position to the target position thereof in the course of the orthodontic treatment; and storing data indicative of the number of steps associated with the given tooth for use in the planning the orthodontic treatment.

Abstract: A method of operation of a compute system includes: qualifying a patient image for analyzing a suspected skin condition; detecting a skin area in the patient image; segmenting the skin area into a segmented image including the suspected skin condition; cropping the segmented image to form a cropped image including the suspected skin condition at a center of the cropped image; analyzing the suspected skin condition to identify a skin disease result and a disease subclass from the cropped image; and assembling a disease identification display including the patient image, a skin disease indication, an image match score, and the disease subclass for displaying on a device.

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: A vascular image processing method performed by a processor includes extracting, from a vascular image, a first vascular region corresponding to an entire blood vessel included in the vascular image, a second vascular region corresponding to a target vessel and one or more branch vessels connected to the target vessel, and a third vascular region corresponding to the target vessel, and predicting a vascular structure in the vascular image, based on the first vascular region, the second vascular region, and the third vascular region, which are extracted from the vascular image.

Abstract: This disclosure describes, in part, techniques for identifying interactions and events associated with inventory locations. For instance, system(s) may receive image data representing a user interacting with an inventory location. The system(s) may then generate heatmap data indicating a first portion of the image data that represents the inventory location and feature data indicating a second portion of the image data that represents the user. Next, the system(s) may analyze the heatmap data with respect to the feature data to determine that the second portion of the image data corresponds to the first portion of the image data. As such, the system(s) may determine that the user is interacting with the inventory location. Based on the determination, the system(s) may analyze the first portion of the image data to identify an event that occurs at the inventory location, such as the user removing an item.

Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: Systems and methods for generative adversarial networks (GANs) to remove artifacts from undersampled magnetic resonance (MR) images are described. The process of training the GAN can include providing undersampled 3D MR images to the generator model, providing the generated example and a real example to the discriminator model, applying adversarial loss, L2 loss, and structural similarity index measure loss to the generator model based on a classification output by the discriminator model, and repeating until the generator model has been trained to remove the artifacts from the undersampled 3D MR images. At runtime, the trained generator model of the GAN can be generate artifact-free images or parameter maps from undersampled MRI data of a patient.

Abstract: A method of operation of a compute system includes: detecting a skin area in a patient image; segmenting the skin area into a segmented image having an acne pimple at the center; generating a target pixel array from the segmented image includes separating a plurality of the acne pimples that are adjacent in the segmented image; identifying an acne characterization of the acne pimples including an area of each acne and an acne score; and assembling a user interface display from the acne characterization for displaying on a device.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.