Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement an intelligent medical image viewing engine. The intelligent medical image viewing engine receives a medical imaging study data structure comprising a plurality of electronic medical images from a medical image database. An image processing component executing within the intelligent medical image viewing engine analyzes the medical imaging study data structure to identify, for each electronic medical image in the plurality of electronic medical images, a corresponding set of image attributes.

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.

Abstract: A mechanism is provided that implements a cognitive data processing system for automatically processing ambiguously labeled data associated with a medical image. The cognitive data processing system receives an ambiguously labeled set of training data in which the ambiguously labeled set of training data comprises portions of data and associated labels, and wherein at least one portion of data in the ambiguously labeled set of training data has a plurality of different labels that together render the portion of data ambiguously labeled. The cognitive data processing system configures an implementation of a model that comprises a loss term, a maximizing term, and a sparsity term. The cognitive data processing system processes the ambiguously labeled set of training data based on the model to identifying a mapping that minimizes a loss function and thereby train the cognitive data processing system.

Abstract: Mechanisms are provided to implement a hybrid deep learning network. The hybrid deep learning network receives, from a imaging system, first input data specifying a non-annotated image. The hybrid deep learning network pre-processes the non-annotated image to generate second input data specifying a hint image and corresponding annotation data specifying salient regions of the hint image. The hybrid deep learning network processes the first input data and second input data to perform training of the hybrid deep learning network by targeting feature detection in the non-annotated image in the salient regions identified in the hint image. The trained hybrid deep learning network is used to process third input data specifying a new non-annotated image to thereby identify an object or structure in the new non-annotated image.

Abstract: A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.

Abstract: A mechanism is provided that implements a cognitive data processing system for automatically processing ambiguously labeled data associated with a medical image. The cognitive data processing system receives an ambiguously labeled set of training data in which the ambiguously labeled set of training data comprises portions of data and associated labels, and wherein at least one portion of data in the ambiguously labeled set of training data has a plurality of different labels that together render the portion of data ambiguously labeled. The cognitive data processing system configures an implementation of a model that comprises a loss term, a maximizing term, and a sparsity term. The cognitive data processing system processes the ambiguously labeled set of training data based on the model to identifying a mapping that minimizes a loss function and thereby train the cognitive data processing system.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations.

Abstract: Mechanisms are provided to implement a generative adversarial network (GAN). A discriminator of the GAN is configured to discriminate input medical images into a plurality of classes including a first class indicating a medical image representing a normal medical condition, a second class indicating an abnormal medical condition, and a third class indicating a generated medical image. A generator of the GAN generates medical images and a training medical image set is input to the discriminator that includes labeled medical images, unlabeled medical images, and generated medical images. The discriminator is trained to classify training medical images in the training medical image set into corresponding ones of the first, second, and third classes. The trained discriminator is applied to a new medical image to classify the new medical image into a corresponding one of the first class or second class. The new medical image is either labeled or unlabeled.

Abstract: Mechanisms are provided to implement a hybrid deep learning network. The hybrid deep learning network receives, from a imaging system, first input data specifying a non-annotated image. The hybrid deep learning network pre-processes the non-annotated image to generate second input data specifying a hint image and corresponding annotation data specifying salient regions of the hint image. The hybrid deep learning network processes the first input data and second input data to perform training of the hybrid deep learning network by targeting feature detection in the non-annotated image in the salient regions identified in the hint image. The trained hybrid deep learning network is used to process third input data specifying a new non-annotated image to thereby identify an object or structure in the new non-annotated image.

Abstract: Mechanisms are provided for evaluating the normality of a medical condition of a patient based on a medical image. A medical image segmentation receives a medical image and segments the medical image to generate an extracted contour representing an anatomical feature. The medical image segmentation engine correlates the extracted contour with a template shape corresponding to the anatomical feature. A feature extraction engine extracts one or more features from a region of the medical image corresponding to the template shape. A normality classification engine performs a normality classification operation on the extracted one or more features to generate a normality score for the medical image and outputs the normality score to a computing device.

Abstract: Medical imaging study summary engine mechanisms are provided. The mechanisms receive a medical imaging study having data representing a plurality of medical images of a patient. The mechanisms generate a temporal trajectory data structure of at least a subset of the medical images in the plurality of medical images, wherein the temporal trajectory data structure specifies topological changes in temporally subsequent medical images in the plurality of medical images. The mechanisms select medical image data corresponding to selected medical images from the medical imaging study data structure based on the temporal trajectory data structure. The mechanisms output the selected medical image data via a medical imaging study user interface.

Abstract: A mechanism is provided in a data processing system to implement a data mapping engine for transparent and declarative translation of search queries on documents to queries on relational data. The data mapping engine receives a query from a search framework for a target database and translates the query to a target schema based on a mapping definition data structure to form a translated query. The mapping definition data structure declaratively maps between a source schema of the search framework to a target schema of a target database. The data mapping engine sends the translated query to the target database and receives a response from the target database. The data mapping engine translates the response to the source schema based on the mapping definition data structure to form a translated response and sends the translated response to the search framework.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: Mechanisms are provided to implement a machine learning framework that operates to register a plurality of machine learning algorithms used to train machine learning models to perform related tasks, and to index the machine learning algorithms to generate and store a machine learning algorithm metadata model for each machine learning algorithm. The machine learning framework receives a user specification of an analytics pipeline task for which a machine learning model is to be trained, and converts the user specification to machine learning algorithm search criteria used to search the index to identify matching machine learning algorithms having a corresponding machine learning algorithm metadata model that matches the machine learning algorithm search criteria. The machine learning framework outputs information describing the matching machine learning algorithms.

Abstract: A method comprises (a) collecting (i) a set of chest computed tomography angiography (CTA) images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple organs; (b) preprocessing the images such that they share the same field of view (FOV); (c) using both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is the summation of the dice scores for all the multiple organs, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the organs; (d) testing a given (input) pre-processed image on the trained network, thereby obtaining segmented output of the given image; and (e) smoothing the segmented output of the given image.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.