Abstract: Methods and systems relating to the field of parallel computing are disclosed herein. The methods and systems disclosed include approaches for sparsity uniformity enforcement for a set of computational nodes which are used to execute a complex computation. A disclosed method includes determining a sparsity distribution in a set of operand data, and generating, using a compiler, a set of instructions for executing, using the set of operand data and a set of processing cores, a complex computation. Alternatively, the method includes altering the operand data. The method also includes distributing the set of operand data to the set of processing cores for use in executing the complex computation in accordance with the set of instructions. Either the altering is conducted to, or the compiler is programmed to, balance the sparsity distribution among the set of processing cores.

Abstract: A system and method for instance-level roadway feature detection for autonomous vehicle control are disclosed.

Abstract: The present disclosure provides a system and method for image data acquisition. The method may include acquiring physiological data of a subject. The physiological data may correspond to a motion of the subject over time. The method may include obtaining a trained machine learning model configured to detect feature data represented in the physiological data. The method may include determining, based on the physiological data, an output result of the trained machine learning model that is generated based on the feature data. The method may include acquiring, based on the output result, image data of the subject using an imaging device.

Abstract: The present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. An example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. The digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.

Abstract: In one embodiment, an MRI apparatus includes: processing circuitry configured to: set a first pulse sequence and a second pulse sequence, wherein, in the first pulse sequence, a first gradient pulse is applied between two adjacent refocusing pulses, and, in the second pulse sequence, a second gradient pulse being different in pulse shape from the first gradient pulse is applied between two adjacent refocusing pulses, wherein: the scanner is configured to acquire first signals and second signals; and the processing circuitry is configured to generate at least one first image and at least one second image; and calculate a T2 value of a body fluid of the object from the at least one first image and the at least one second image in such a manner that influence of movement including diffusion of the body fluid is removed.

Abstract: A system to manage incoming communication is described. The system includes a multimedia gateway configured to handle real-time multimedia communications and a communication management server with a master AI agent that receives a notification of an incoming communication associated with a user device from a plurality of user devices. The master AI agent creates a current state for the user environment based on a context associated with the incoming communication, user preferences, and an interaction graph. An action selection function (ASF) is invoked to process the incoming communication, wherein the ASF is associated with an environment state model that implements a Markov Decision Process (MDP) to reflect different states, actions, and associated rewards, wherein the action selection function is configured to determine an action for the incoming communication. The action is executed by the multimedia gateway or an AI communication agent.

Abstract: In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

Abstract: Disclosed herein is a system and method for obtaining and generating motion capture datasets relating to various working activities for ergonomic risk assessment. An example system may comprise a computing device that obtains first data from multiple motion capture cameras, obtains second data from multiple visible light imaging sensors, calculates 3D positions of multiple reflective markers positioned on several subjects performing various working activities based on the first data, labels each marker to generate marker trajectories, performs gap filing and smoothing functions on the marker trajectories to generate global marker positions, transforms the global marker positions into a corresponding image coordinate system of each sensor to generate 3D pose data of the subjects at each sensor viewpoint, projects the 3D pose data into frames of the second data to generate 2D pose data, and generates a dataset comprising the second data, the 2D pose data, and the 3D pose data.

Abstract: An apparatus and a method for segmenting a steel microstructure phase are provided. The apparatus includes a storage configured for storing a machine learning algorithm and a processing device that segments a microstructure phase using the machine learning algorithm. The processing device is configured to receive label data, to learn a machine learning model by use of the label data as learning data for the machine learning model, and to segment a phase of a steel microstructure image by use of the learned machine learning model.

Abstract: A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets.

Abstract: The present invention relates to an automatic cervical cancer diagnosis system for performing machine learning by classifying cervical data required for automatic diagnosis of cervical cancer according to accurate criteria and automatically diagnosing cervical cancer based on the machine learning, the automatic cervical cancer diagnosis system including: a learning data generator configured to classify unclassified photographed image data for a cervix transmitted from an external device or a storage according a combination of multi-level classification criteria to generate learning data for each new classification criterion in a learning mode; a photographed image pre-processer configured to pre-process photographed cervix images; a cervical cancer diagnoser including a machine learning model for cervical cancer that learns a characteristic of the learning data generated for each classification criterion in the learning mode, wherein the machine learning model generates diagnosis information about whether cer

Abstract: Endoscopic systems including multimode optical fibers for and methods of interrogating a portion of a body are described. In an example, the endoscopic system includes a multimode optical fiber, a coherent light source positioned to emit coherent light through the multimode optical fiber, and a photodetector positioned to absorb scattered coherent light emitted from the multimode optical fiber and configured to generate a speckled light signal based on the scattered coherent light. In an example, the endoscopic system includes a controller configured to analyze the speckled light signal with machine learning classifier and to generate an identification signal indicative of a characteristic of the portion of the body.

Abstract: Methods and apparatuses for assessing oral health and automatically providing diagnosis of one or more oral diseases. Described herein are intraoral scanning methods and apparatuses for collecting and analyzing image data and to detect and visualize features within image data that are indicative of oral diseases or conditions, such as gingival inflammation or oral cancer. These methods and apparatuses may be used for identifying and evaluating lesions, redness and inflammation in soft tissue and caries and cracks in the teeth. The methods can include training a machine learning model and using the trained machine learning model to provide a diagnosis of an oral disease or condition based on image data collected using multiple scanning modes of an intraoral scanner.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

Abstract: An image processing method of the present disclosure may include receiving a scanned image, and processing the received image through an octave convolution-based neural network to output a high-quality image and an edge image for the received image. The octave convolution-based neural network may include a plurality of octave encoder blocks and a plurality of octave decoder blocks. Each octave encoder block may include an octave convolutional layer, and may be configured to output a high-frequency feature map and a low-frequency feature map for the image.

Abstract: A cardiac device comprises a memory arranged for receiving haemodynamic data, and a computer arranged for applying a cardiovascular model comprising a cardiac model and an arterial and venous blood circulation model using the data received in the memory, and for extracting therefrom at least one cardiac activity indicator (CI).

Abstract: A method is performed by a computing system. The method includes receiving image data from an imaging device, and determining, using the image data, a plurality of image-space tools, each image-space tool associated with a tool of a plurality of tools, each tool controlled by a manipulator of a plurality of manipulators. The method further includes determining a first correspondence between a first image-space tool of the plurality of image-space tools and a first tool of the plurality of tools based on a first disambiguation setting associated with the first tool.

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for generating a convolutional neural network (CNN) are described. In one aspect, a minimal CNN having, e.g., three or more layers is trained. Cascade training may be performed on the trained CNN to insert one or more intermediate layers until a training error is less than a threshold. When cascade training is complete, cascade network trimming of the CNN output from the cascade training may be performed to improve computational efficiency. To further reduce network parameters, convolutional filters may be replaced with dilated convolutional filters with the same receptive field, followed by additional training/fine-tuning.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: A medical image processing method performed by a computer, for measuring the spatial location of a point on the surface of a patient's body including: acquiring at least two two-dimensional image datasets, wherein each two-dimensional image dataset represents a two-dimensional image of at least a part of the surface which comprises the point, and wherein the two-dimensional images are taken from different and known viewing directions; determining the pixels in the two-dimensional image datasets which show the point on the surface of the body; and calculating the spatial location of the point from the locations of the determined pixels in the two-dimensional image datasets and the viewing directions of the two-dimensional images; wherein the two-dimensional images are thermographic images.

Abstract: A system and method for instance-level lane detection for autonomous vehicle control are disclosed.

Abstract: A method and a system automatically generate a digital representation of an annulus structure of a valve from a segmented digital representation of a human internal heart. The basis for the segmented digital representation is multi-slice computed tomography image data. The method includes automatically determining, for at least a first effective time point, based on a segmentation, i.e. labels, of a provided input segmented digital representation, a candidate plane, and/or a candidate orientation vector together with a candidate center point, arranged with respect to the input segmented digital representation for the first effective time point, and candidate points for the annulus structure are determined automatically. From the candidate points acting as support points, a candidate spline interpolation is generated which is then adapted based on the input segmented digital representation.

Abstract: Disclosed are methods and systems for: (i) sequentially illuminating a specimen with different spatial distributions of light, wherein each illumination causes an object embedded in the specimen to emit radiation in response to the light; (ii) for each different spatial distribution of illumination light, imaging the radiation emitted from the specimen from each of multiple sides of the specimen; and (iii) determining information about the object in the specimen based on the imaged radiation from each of the multiple sides for each of the different spatial distributions of illumination light.

Abstract: An embodiment of the present disclosure provides a weight precision configuration method, including: determining a pre-trained preset neural network including a plurality of layers each having a preset weight precision; reducing, based on a current threshold, the weight precision of at least one layer in the preset neural network to obtain a corrected neural network having a recognition rate greater than the current threshold; and reducing the weight precision of a layer includes: adjusting the weight precision of the layer; setting, if a termination condition is met, the weight precision of the layer to a corrected weight precision that is less than or equal to the preset weight precision of the layer; and returning, if the termination condition is not met, to the operation of adjusting the weight precision of the layer; and determining a final weight precision of each layer to obtain a final neural network.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.

Abstract: A software system for assisting a physician's diagnosis and reporting based on medical imaging includes software tools for pre-processing medical images, collecting findings, and automatically generating medical reports. A pre-processing software component generates an anatomical segmentation and/or computer-aided diagnosis based on an analysis of a medical image. A finding collecting software component displays the image, and facilitates rapid and efficient entry of associated findings by displaying a filtered list of templates associated with a selected region of the image and/or a computer-aided diagnosis. When the physician selects a template from the filtered list, the template may be displayed with entry options pre-filled based, e.g., on any computer-aided diagnosis. After the physician edits and/or confirms the entries, a report generation component uses the entries to generate a medical report.

Abstract: A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient's brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient's brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.

Abstract: A method of and an Artificial Intelligence (AI) system for predicting hemodynamic parameters for a target vessel, in particular of an aorta, as well as to a computer-implemented method of training an AI unit of the AI system are disclosed. A vessel shape model of the target vessel and a corresponding flow profile of the target vessel are received. At least one hemodynamic parameter is predicted by the AI unit based on the received vessel shape model and the received flow profile. The AI unit is arranged and configured to predict at least one hemodynamic parameter based on a received vessel shape model and a received flow profile of the target vessel (aorta).

Abstract: Described herein are means for learning semantics-enriched representations via self-discovery, self-classification, and self-restoration in the context of medical imaging. Embodiments include the training of deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a collection of semantics-enriched pre-trained models, called Semantic Genesis. Other related embodiments are disclosed.

Abstract: A machine receives a first set of global parameters from a global parameter server. The first set of global parameters includes data that weights one or more operands used in an algorithm that models an entity type. Multiple learner processors in the machine execute the algorithm using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

Abstract: Provided is a obtaining an excitation-emission matrix, wherein the excitation-emission matrix is measured with a spectrometer by: illuminating a biological tissue with stimulant light at a first wavelength to cause a first fluorescent emission of light by the biological tissue, measuring a first set of intensities of the first fluorescent emission of light at a plurality of different respective emission wavelengths, illuminating the biological tissue with stimulant light at a second wavelength to cause a second fluorescent emission of light by the biological tissue, and measuring a second set of intensities of the second fluorescent emission of light at a plurality of different respective emission wavelengths; and inferring a classification of the biological tissue or a concentration of a substance in the biological tissue with a multi-layer neural network or other machine learning model.

Abstract: Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Abstract: Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like.

Abstract: A predictor has a memory which stores at least one example for which an associated outcome is not known. The memory stores at least one decision tree comprising a plurality of nodes connected by edges, the nodes comprising a root node, internal nodes and leaf nodes. Individual ones of the nodes and individual ones of the edges each have an assigned module, comprising parameterized, differentiable operations, such that for each of the internal nodes the module computes a binary outcome for selecting a child node of the internal node. The predictor has a processor configured to compute the prediction by processing the example using a plurality of the differentiable operations selected according to a path through the tree from the root node to a leaf node.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.

Abstract: Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). In some embodiments, the compiler determines whether sparsity requirements of channels implemented on individual cores are met on each core. If the sparsity requirement is not met, the compiler, in some embodiments, determines whether the channels of the filter can be rearranged to meet the sparsity requirements on each core and, based on the determination, either rearranges the filter channels or implements a solution to non-sparsity.

Abstract: A medical image data processing apparatus is provided and includes processing circuitry to receive medical image data in respect of at least one subject; receive non-image data; generate a filter based on the non-image data; and apply the filter to the medical image data, wherein the filter limits a region of the medical image data.

Abstract: Methods, apparatuses, and systems for providing real-time surgical assistance to a surgical robot using an imaging module, a context computer module, and a quantum analysis module are disclosed. The imaging module receives one or more data related to a patient from an intra-operative database and performs a first step analysis based on the received data. The context computer module determines a type of resource required for analysis based on data received from a historical database and the intra-operative database. The quantum analysis module receives data from the imaging module and the context computer module. The quantum analysis module receives real-time data from operation room (OR) equipment and performs quantum analysis on the received data and real-time data. The quantum analysis module facilitates real-time surgical assistance and recommendations to the surgical robot.

Abstract: The present invention relates to a system for efficient large-scale data distribution in a distributed and parallel processing environment. In particular, the present invention relates to global Top-k sparsification for low bandwidth networks. The present invention verifies that gTop-k S-SGD has nearly consistent convergence performance with S-SGD and evaluates the training efficiency of gTop-k on a cluster with 32 GPU machines which are inter-connected with 1 Gbps Ethernet. The experimental results show that the present invention achieves up to 2.7-12× higher scaling efficiency than S-SGD with dense gradients, and 1.1-1.7× improvement than the existing Top-k S-SGD.

Abstract: The disclosure relates to systems and methods for image processing. A trained deep learning model may be determined. An input image may be acquired. A processing result may be generated by processing the input image based on the trained deep learning model. The trained deep learning model may be obtained according to a process including: acquiring a preliminary deep learning model; acquiring a sample image; generating a plurality of sub-images based on the sample image; and training the preliminary deep learning model based on the plurality of sub-images to obtain the trained deep learning model.

Abstract: Various methods and systems are provided for transforming a style of an image into a different style while preserving content of the image. In one example, a method includes transforming a first image acquired via a medical imaging system into a second image based on visual characteristics of a third image using a system of deep neural networks configured to separate visual characteristics from content of an image, where the second image includes a content of the first image and the visual characteristics of the third image and the first and second images have different visual characteristics. The transformed second image may then be presented to a user.

Abstract: A method and a system for acquiring a 3D ultrasound image. The method includes receiving a request to capture a plurality of ultrasound image for a medical test corresponding to a medical condition. The method further includes determining a body part corresponding to the medical test. Further, the method includes identifying an imaging site particular to the medical test. Furthermore, the method includes providing a navigational guidance to the user in real time for positioning a handheld ultrasound device. Subsequently, the user is assisted to capture the plurality of ultrasound image of the imaging site in real time using deep learning. Further, the plurality of ultrasound images of the imaging site is captured. Finally, the method includes converting the plurality of ultrasound image to a 3-Dimensional (3D) ultrasound image in real time.

Abstract: One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Abstract: A medical scan triaging system is operable to generate a global abnormality probability for each of a plurality of medical scans by utilizing a computer vision model trained on a training set of medical scans. A triage probability threshold is determined based on user input to a client device. A first subset of the plurality of medical scans, designated for human review, is determined by identifying medical scans with a corresponding global abnormality probability that compares favorably to the triage probability threshold. A second subset of the plurality of medical scans, designated as normal, is determined by identifying ones of the plurality of medical scans with a corresponding global abnormality probability that compares unfavorably to the triage probability threshold. Transmission of the first subset of the plurality of medical scans to a plurality of client devices associated with a plurality of users is facilitated.

Abstract: A discriminator includes a common learning unit and a plurality of learning units that are connected to an output unit of the common learning unit. The discriminator is trained, using a plurality of data sets of a first image obtained by capturing an image of a subject that has developed a disease and an image data of a disease region in the first image, such that information indicating the disease region is output from a first learning unit in a case in which the first image is input to the common learning unit. In addition, the discriminator is trained, using a plurality of data sets of an image set obtained by registration between the first image and a second image whose type is different from the type of the first image, such that an estimated image of the second image is output from an output unit of a second learning unit.

Abstract: An embodiment of this application provides an image object detection method. The method may include obtaining a detection image, an n-level deep feature map framework, and an m-level non-deep feature map framework. The method may further include extracting deep feature from an (i?1)-level feature of the detection image using an i-level deep feature map framework, to obtain an i-level feature of the detection image. The method may further include extracting non-deep feature from a (j?1+n)-level feature of the detection image using a j-level non-deep feature map framework, to obtain a (j+n)-level feature of the detection image. The method may further include performing information regression operation on an a-level feature to an (m+n)-level feature of the detection image, to obtain an object type information and an object position information of an object in the detection image. The a is an integer less than n and greater than or equal to 2.

Abstract: Techniques are described for content-adaptive downsampling of digital images and videos for computer vision operations, such as semantic segmentation. A computer vision system comprises a memory, one or more processors operably coupled to the memory and a downsampling module configured for execution by the one or more processors to perform, based on a non-uniform sampling model trained to predict content-aware sampling parameters, downsampling input image data to generate downsampled image data. A segmentation module is configured for execution by the one or more processors to perform segmentation on the downsampled image to produce a segmentation result, such as a feature map that assigns pixels of the downsampled image data to object classes. An upsampling module is configured for execution by the one or more processors to perform upsampling according to the segmentation result to produce upsampled image data.

Abstract: Provided is a method based on deep neural network to extract appearance and geometry features for pulmonary textures classification, which belongs to the technical fields of medical image processing and computer vision. Taking 217 pulmonary computed tomography images as original data, several groups of datasets are generated through a preprocessing procedure. Each group includes a CT image patch, a corresponding image patch containing geometry information and a ground-truth label. A dual-branch residual network is constructed, including two branches separately takes CT image patches and corresponding image patches containing geometry information as input. Appearance and geometry information of pulmonary textures are learnt by the dual-branch residual network, and then they are fused to achieve high accuracy for pulmonary texture classification. Besides, the proposed network architecture is clear, easy to be constructed and implemented.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from an imaging device. The image may be a medical image. The computing device may detect one or more potential indicators of disease in the image using a first algorithm and determine areas of potential disease in the image using an artificial intelligence algorithm. The computing device may determine a correlation between the determined areas of potential disease in the image and the one or more potential indicators of disease for the image. The computing device may, in response to determining a positive correlation, identify one or more of the potential indicators of disease for annotation and generate a report indicating one or more potential indicators of disease was found in the image.

Abstract: Disclosed are provided systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging. For example, a system having means to execute a neural network model formed from a plurality of layer blocks including an encoder layer block which precedes a plurality of decoder layer blocks includes: means for associating a resolution value with each of the plurality of layer blocks; means for processing via the encoder layer block a respective layer block input including a down-sampled layer block output processing, via decoder layer blocks, a respective layer block input including an up-sampled layer block output and a layer block output of a previous layer block associated with a prior resolution value of a layer block which precedes the respective decoder layer block; and generating the respective layer block output by summing or concatenating the processed layer block inputs.