Abstract: In medicine and dentistry, image quality affects computer vision accuracy. However, some problems are more tolerant of noise depending on disease severity and radiographic obviousness. There is a need to have a noise estimation model that adapts to each specific domain. A noise estimation model is trained to output a set of domain noise estimates for an input image, each estimate indicating an impact of noise present in the input image on a particular domain, e.g. labeling of a dental feature such as a dental anatomy, pathology, or treatment. The noise estimation model is trained by processing image pairs with a set of machine learning models for a plurality of domains, the image pairs including a raw image and a modified image obtained by adding noise to the raw image. Outputs of the set of machine learning models for the raw and modified images are compared to obtain measured noise metrics. The noise estimation model processes the modified image and is trained to estimate noise metrics.

Abstract: A system for video surveillance includes an image sensor configured capture an image including a reference zone and a memory device that stores instructions. The system also includes one or more processors that are configured to execute the instructions to determine 3D coordinates of a target comprised in the image and determine 3D coordinates of the reference zone. The one or more processors are further configured to identify an event according to the 3D coordinates of the target and the 3D coordinates of the reference zone.

Abstract: A comprehensive dental readiness platform is presented. Dental patient data including an image, proposed treatments, and a dental form are received and processed by first machine learning models to obtain clinical findings and predicted values for fields of the dental form. The clinical findings and other results are processed by a second machine learning model to obtain predictions of a future dental condition of a patient. The second machine learning model utilizes an ensemble of Transformer Neural Networks, Long-Short-Term-Memory Networks, Convolutional Neural Networks, and Tree-Based Algorithms to predict the dental readiness classification, dental readiness durability, dental readiness error, dental emergency likelihood, prognosis, and alternative treatment options.

Abstract: For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.

Abstract: An image processing method for performing image alignment includes: acquiring a moving image generated by a first imaging modality; acquiring a fixed image generated by a second imaging modality; jointly optimizing a generator model, a register model, and a segmentor model applied to the moving image and the fixed image according to a plurality of cost functions; and applying a spatial transformation corresponding to the optimized register model to the moving image to align the moving image to the fixed image; wherein: the generator model generates a synthesized image from the moving image conditioned on the fixed image; the register model estimates the spatial transformation to align the synthesized image to the fixed image; and the segmentor model estimates segmentation maps of the moving image, the fixed image, and the synthesized image.

Abstract: Technologies for determining the accuracy of three-dimensional models include a device having circuitry to obtain two-dimensional images of an anatomical object (e.g., a bone of a human joint), to obtain a candidate three-dimensional model of the anatomical object, and to produce two-dimensional silhouettes of the candidate three-dimensional model. The circuitry is also to apply an edge detection algorithm to the two-dimensional images to produce corresponding edge images and to compare the two-dimensional silhouettes to the edge images to produce a score indicative of an accuracy of the candidate three-dimensional model.

Abstract: Method, device and machine for calculating an index representative of properties of a material of the bone type of an individual to be subjected to tests, particularly wherein the method includes a first acquisition step for acquiring at least one image having a plurality of elementary units of a sample of the material, wherein a generation step is provided for generating a grid of elementary geometric elements, or cells, which is associated with, in particular superimposed on, the image, an image processing step in which it is provided to calculate at least the apparent elastic modulus and a density coefficient of the material, both as a function of characteristic values of each cell, and a calculation step for calculating the index representative of the properties of a material, wherein the index is a function of the value of the apparent elastic modulus net of the contribution of the density coefficient.

Abstract: A machine-based learning method estimates a probability of bone fractures in a 3D image, more specifically vertebral fractures. The method and system utilizing such method utilize a data-driven computational model to learn 3D image features for classifying vertebra fractures. A three-dimensional medical image analysis system for predicting a presence of a vertebral fracture in a subject includes a 3D image processor for receiving and processing 3D image data of a 3D image of the subject, producing two or more sets of 3D voxels. Each of the sets of 3D voxels corresponds to an entirety of the 3D image and each of the sets of 3D voxels consists of equal 3D voxels of different dimensions. The system also includes a voxel classifier for assigning the 3D voxels one or more class probabilities each of the 3D voxels contains a fracture using a computational model, and a fracture probability estimator for estimating a probability of the presence of a vertebral fracture in the subject.

Abstract: A system and method includes input of a plurality of sets of training data to a neural network to generate a plurality of sets of output data, determination of a first loss based on the plurality of sets of output data and on the plurality of sets of ground truth data, determination if a second loss based on the plurality of sets of output data and one or more physics-based constraints, and modification of the neural network based on the first loss and the second loss.

Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.

Abstract: Methods, systems, and media for segmenting images are provided. In some embodiments, the method comprises: generating an aggregate U-Net comprised of a plurality of U-Nets, wherein each U-Net in the plurality of U-Nets has a different depth, wherein each U-Net is comprised of a plurality of nodes Xi,j, wherein i indicates a down-sampling layer the U-Net, and wherein j indicates a convolution layer of the U-Net; training the aggregate U-Net by: for each training sample in a group of training samples, calculating, for each node in the plurality of nodes Xi,j, a feature map xi,j, wherein xi,j is based on a convolution operation performed on a down-sampling of an output from Xi?1,j when j=0, and wherein xi,j is based on a convolution operation performed on an up-sampling operation of an output from Xi+1,j?1 when j>0; and predicting a segmentation of a test image using the trained aggregate U-Net.

Abstract: Training a generator includes processing a dental image using the generator to obtain a synthetic pathology label, such has a pixel mask indicating portions of the dental image representing caries. The synthetic pathology label is compared to a target pathology label for the dental image and the generator is updated according to the comparison. The synthetic pathology may be evaluated by a discriminator along with a real pathology label to obtain a realism estimate. The discriminator and generator may be updated according to accuracy of the realism estimate. Inputs to the generator may further include tooth labels and/or labels of restorations. Machine learning models may be trained to label restorations and defects in restorations. A machine learning model may be trained to identify the surface of a tooth having a pathology thereon.

Abstract: Disclosed is a system and method for assembling instrument sets that include correct instruments with one or more verified states for different procedures. The system may receive a request for a particular instrument, and may determine instrument states defined for the particular instrument or a procedure involving the particular instrument. The system may scan a first instrument using one or more sensors, may verify that the first instrument matches a make, model, or type of the particular instrument based on the scanning data, and may classify the first instrument states with at least a threshold probability based on the scanning data matching characteristics from a probabilistic model. The system may control the distribution of the first instrument to a first destination or a second destination based on whether or not the first instrument states satisfy the instrument states defined for the particular instrument or the procedure.

Abstract: A method of fundus oculi image analysis includes acquiring a target fundus oculi image; analyzing the target fundus oculi image by a fundus oculi image analysis model determined by training to acquire an image analysis result of the target fundus oculi image; and the fundus oculi image analysis model includes at least one of an image overall grade prediction sub-model and an image quality factor sub-model. The method performs quality analysis on the target fundus oculi image by the fundus oculi image analysis model, and when the model includes the overall grade prediction sub-model, a prediction result of whether the target fundus oculi image as a whole is gradable can be acquired; when the model includes the image quality factor sub-model, the analysis result of the fundus oculi image quality factor can be acquired and the image analysis model is determined by extensive image training, and the reliability of the result of whether the image is gradable determined based on the above model is high.

Abstract: Systems and methods are provided for reconstructing a three-dimensional result image data set from computed tomography from a plurality of two-dimensional images that create an image of an object undergoing examination from a particular imaging angle, The imaging angles of all the images lie within a restricted angular range. A three-dimensional artifact-reduced image data set is provided based on the two-dimensional images using an algorithm for reducing artifacts that are the result of a restriction of the angular range. The result image data set is reconstructed using a reconstruction algorithm that processes both the artifact-reduced image data set and the two-dimensional images as input data.

Abstract: A gate apparatus includes: an exit gate door; a first biometrics information acquisition unit that acquires, from a user who moves toward the exit gate door in a closed state, first target biometrics information to be compared with registered biometrics information registered in advance; a second biometrics information acquisition unit that acquires second target biometrics information to be compared with the registered biometrics information from the use who stops in front of the exit gate door when there is no matching in a comparison between the first target biometrics information and the registered biometrics information or the comparison is unable to be performed; and a door control unit that opens the closed exit gate door in accordance with a result of a comparison between the first target biometrics information or the second target biometrics information and the registered biometrics information.

Abstract: A control apparatus includes a reception unit that receives a plurality of reduced images included in a radiographic image in stages from a radiographic imaging apparatus, and a display control unit that, in a case where the radiographic image meets a pre-determined standard, displays a first image generated from the reduced images on a display unit, and in a case where the radiographic image does not meet the pre-determined standard, displays on the display unit a second image generated from more reduced images than the reduced images from which the first image is generated.

Abstract: A data processing apparatus and a method, a recognition apparatus, a learning data storage apparatus, a machine learning apparatus, and a program capable of improving recognition accuracy for data of a rare case are provided. A data processing apparatus according to one aspect of the present invention includes a recognition unit that learns using a learning data set, a recognition result correction unit that corrects a recognition result of the recognition unit for data acquired through a data acquisition unit in accordance with an instruction from a user, and a machine learning unit that performs learning of the recognition unit using the data in which the recognition result is corrected. The machine learning unit performs learning of the recognition unit by setting a degree of contribution to learning from the data in which the recognition result is corrected to be higher than a degree of contribution to learning of the recognition unit from learning data included in the learning data set.

Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.

Abstract: Disclosed herein are methods and system for training artificial intelligence models configured to execute image segmentation techniques. The methods and system describe a server that receives a first image including a set of pixels depicting multiple objects. The server also receives a second image having a second set of pixels depicting the same set of objects. The server then analyzes the pixels from the first and second images. When a difference between at least one visual attribute of a pixel within the second image and a corresponding pixel within the first image satisfies a predetermined threshold, it will be encoded as spikes to send to the model, the model will be trained using supervised STDP rule by revising weights associated with the nodes within the AI model where the node corresponds to the pixels within the first and/or the second image.