Abstract: Systems and techniques for facilitating a deep learning architecture for automated image feature extraction are presented. In one example, a system includes a machine learning component. The machine learning component generates learned imaging output regarding imaging data based on a convolutional neural network that receives the imaging data. The machine learning component also performs a plurality of sequential and/or parallel downsampling and upsampling of the imaging data associated with convolutional layers of the convolutional neural network.

Abstract: Systems and techniques for training an auto-encoder on a single class are presented. In one example, a system trains an auto-encoder based on first data associated with a first class to generate a trained auto-encoder. The system also applies, using a multiplier, gain data indicative of a gain value to second data associated with the first class and third data associated with a second class to generate enhanced input data that represents a differentiation between the second data associated with the first class and the third data associated with the second class. An input enhancer comprises the trained auto-encoder and the multiplier. Furthermore, the system trains a convolutional neural network based on the enhanced input data to generate a trained convolutional neural network. The system also classifies the first class and the second class based on the input enhancer and the trained convolutional neural network.

Abstract: Systems and techniques for facilitating a deep convolutional neural network with self-transfer learning are presented. In one example, a system includes a machine learning component, a medical imaging diagnosis component and a visualization component. The machine learning component generates learned medical imaging output regarding an anatomical region based on a convolutional neural network that receives medical imaging data. The machine learning component also performs a plurality of sequential downsampling and upsampling of the medical imaging data associated with convolutional layers of the convolutional neural network. The medical imaging diagnosis component determines a classification and an associated localization for a portion of the anatomical region based on the learned medical imaging output associated with the convolutional neural network.

Abstract: An imaging system includes an imaging unit, a display unit, and at least one processor. The at least one processor is configured to acquire a first type of diagnostic imaging information of the patient; reconstruct a first image using the first type of diagnostic imaging information; if a first stop criterion for terminating imaging is not satisfied, acquire a second type of diagnostic imaging information having an increased level of acquisitional burden; reconstruct a second image; if a second stop criterion for terminating imaging is not satisfied, acquire a third type of diagnostic imaging information having an increased level of acquisitional burden, wherein the patient is maintained on a table of the imaging unit during the acquisition of the second type of diagnostic imaging information, reconstruction of the second image, and acquisition of the third type of diagnostic imaging information; reconstruct a third image; and display the third image.

Abstract: Systems and techniques for facilitating a deep convolutional neural network with self-transfer learning are presented. In one example, a system includes a machine learning component, a medical imaging diagnosis component and a visualization component. The machine learning component generates learned medical imaging output regarding an anatomical region based on a convolutional neural network that receives medical imaging data. The machine learning component also performs a plurality of sequential downsampling and upsampling of the medical imaging data associated with convolutional layers of the convolutional neural network. The medical imaging diagnosis component determines a classification and an associated localization for a portion of the anatomical region based on the learned medical imaging output associated with the convolutional neural network.

Abstract: Systems and techniques for facilitating a deep convolutional neural network with self-transfer learning are presented. In one example, a system includes a machine learning component, a medical imaging diagnosis component and a visualization component. The machine learning component generates learned medical imaging output regarding an anatomical region based on a convolutional neural network that receives medical imaging data. The machine learning component also performs a plurality of sequential downsampling and upsampling of the medical imaging data associated with convolutional layers of the convolutional neural network. The medical imaging diagnosis component determines a classification and an associated localization for a portion of the anatomical region based on the learned medical imaging output associated with the convolutional neural network.

Abstract: Systems and techniques for facilitating a deep learning architecture for automated image feature extraction are presented. In one example, a system includes a machine learning component. The machine learning component generates learned imaging output regarding imaging data based on a convolutional neural network that receives the imaging data. The machine learning component also performs a plurality of sequential and/or parallel downsampling and upsampling of the imaging data associated with convolutional layers of the convolutional neural network.

Abstract: Systems and techniques for training an auto-encoder on a single class are presented. In one example, a system trains an auto-encoder based on first data associated with a first class to generate a trained auto-encoder. The system also applies, using a multiplier, gain data indicative of a gain value to second data associated with the first class and third data associated with a second class to generate enhanced input data that represents a differentiation between the second data associated with the first class and the third data associated with the second class. An input enhancer comprises the trained auto-encoder and the multiplier. Furthermore, the system trains a convolutional neural network based on the enhanced input data to generate a trained convolutional neural network. The system also classifies the first class and the second class based on the input enhancer and the trained convolutional neural network.

Abstract: Systems and techniques for facilitating image analysis using deviation from normal data are presented. In one example, a system generates atlas map data indicative of an atlas map that includes a first portion of patient image data from a plurality of reference patients and a second portion of the patient image data from a plurality of target patients. The first portion of the patient image data is matched to a corresponding age group for a set of patient identities associated with the first portion of the patient image data. The system also generates deviation map data that represents an amount of deviation for the second portion of the patient image data compared to the first portion of the patient image data. Furthermore, the system trains a neural network based on the deviation map data to determine one or more clinical conditions.

Abstract: Systems and techniques for building a binary neural network architecture are presented. In one example, a system trains a neural network based on a data set to form a first neural network of a binary neural network architecture and determine whether a first class exists. The system also trains a copy of the first neural network based on the data set to form a second neural network of the binary neural network architecture and determine whether a second class exists. Furthermore, the system trains a copy of the second neural network based on the data set to form an Mth neural network of the binary neural network architecture and determine whether an Mth class exists, where M is an integer greater than or equal to three.

Abstract: A system includes an input unit, a data store, at least one processor, and a display unit. The input unit is configured to obtain operational parameters relating to the performance of at least one process during a therapeutic cycle of a patient. The data store includes reference values of the operational parameters that correspond to at least one known patient outcome. The at least one processor is operably coupled to the input unit, and is configured to evaluate the operational parameters to determine a patient process progress state based on a comparison between values of the operational parameters and corresponding reference values. The display unit is operably coupled to the at least one processor, and is configured to display information corresponding to the patient process progress state.

Abstract: An imaging system includes an imaging unit, a display unit, and at least one processor. The at least one processor is configured to acquire a first type of diagnostic imaging information of the patient; reconstruct a first image using the first type of diagnostic imaging information; if a first stop criterion for terminating imaging is not satisfied, acquire a second type of diagnostic imaging information having an increased level of acquisitional burden; reconstruct a second image; if a second stop criterion for terminating imaging is not satisfied, acquire a third type of diagnostic imaging information having an increased level of acquisitional burden, wherein the patient is maintained on a table of the imaging unit during the acquisition of the second type of diagnostic imaging information, reconstruction of the second image, and acquisition of the third type of diagnostic imaging information; reconstruct a third image; and display the third image.

Abstract: A system and method for analyzing clinical data includes a reference database comprising a stored set of reference data comprising enumerated results of a reference population for a clinical test. The system also includes a patient database comprising a stored set of clinical data corresponding to a patient result for the clinical test that is selected from the enumerated results. A processor is included in the system and is programmed to access the patient and reference databases, identify a distribution of reference data over the enumerated test results, and calculate a relevance index based on the distribution of reference data. The processor is further programmed to compare the patient result to the distribution of reference data, calculate a disagreement index based on the comparison, and calculate a deviation index from the relevance and disagreement indices. A graphical user interface is also included to output a visualization of the deviation index.

Abstract: A variety of systems, methods, and articles of manufacture are disclosed. An example includes accessing of patient deviation scores indicative of differences between patient data and reference data representative of a population segment, wherein the patient deviation scores are derived from longitudinal data of the patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data; identifying a trend in the patient deviation scores for at least one clinical parameter; generating of a report including a visual indication of the trend; and outputting of the report. The report includes one or more views including Z, T, D, DT, and D feedback on T views, using image and non-image data.

Abstract: A system comprising a memory device having a plurality of routines stored therein, a processor configured to execute the plurality of routines stored in the memory device, the plurality of routines comprising: a routine configured to effect, when executed, accessing of patient deviation scores indicative of differences between patient data and reference data representative of a population segment, wherein the patient deviation scores are derived from longitudinal data of the patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data; a routine configured to effect, when executed, identifying a trend in the patient deviation scores for at least one clinical parameter; a routine configured to effect, when executed, generating of a report including a visual indication of the trend; and a routine configured to effect, when executed, outputting of

Abstract: An autonomous medical imaging system includes at least one autonomous imaging subsystem and at least one autonomous detection subsystem. The autonomous detection subsystem is configured to communicate with the autonomous imaging subsystem, and the autonomous imaging subsystem is configured to communicate with the autonomous detection subsystem.

Abstract: A system and method for analyzing and visualizing a local feature of interest includes access of a clinical image dataset comprising clinical image data acquired from a patient, identification of a region of interest (ROI) from the clinical image dataset, and extraction of at least one local feature corresponding to the ROI. The system and method also include definition of a local feature dataset comprising data representing at least one local feature, access of a pre-computed reference dataset comprising image data representing an expected value of the at least one identified derived characteristic of interest, and comparison of the characteristic dataset to the pre-computed reference dataset. Further, the system and method include calculation of at least one deviation metric from the comparison and output of a visualization of the at least one deviation metric.

Abstract: A modular control system including a plurality of individual touch screen devices, each touch screen device including a display unit, a touch input, a computing device, a network connection, and a programming logic for controlling a remote system and displaying a status of the remote system on the touch screen device, the remote system having network connectivity to enable the remote system to exchange information with and respond to instructions from the touch screen devices, the touch screen devices configured for automatic self-synchronization based on a status of the remote system or a status of at least one of the touch screen devices.

Abstract: Systems and methods are disclosed for managing autonomous detector and imager subsystems communicating as nodes on a network. In an embodiment, an application server is provided which coordinates use of the autonomous imager and detector subsystems so that appropriate combinations of imagers and detectors are used for particular imaging applications. In another embodiment, the performance of a detector subsystem may be automatically evaluated prior to use, such as by one or more automated routines that monitor or measure factors related to detector performance. Additional systems, methods, and devices are also disclosed.

Abstract: A system and method for analyzing and visualizing spectral CT data includes access of a set of image data acquired from a patient comprising spectral CT data, identification of a plurality of target regions of interest (TROIs) and a reference region of interest (RROI) from the set of image data, extraction of a plurality of target spectral Hounsfield unit (HU) curves from image data representing the plurality of TROIs, extraction of a reference spectral HU curve from image data representing the RROI, normalization of the plurality of target spectral HU curves with respect to the reference spectral HU curve, and display of the plurality of normalized target spectral HU curves.