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: A method for processing medical images includes analyzing a medical image to detect a medical condition from a list of medical conditions, wherein the list of medical conditions includes aortic dissection, pulmonary embolism, and coronary stenosis. Responsive to determining the medical image includes a first medical condition, the method generates a first report that includes information on a detection of the first medical condition. The method identifies, a medical specialist based on availability and medical expertise and sends to the identified medical specialist, the medical image and the first report for a decision on the detection of the first medical condition. Responsive to receiving the decision from the medical specialist, the method sends to a second electronic device, the decision, the medical image, and the first report.

Abstract: A method for processing medical images includes analyzing a medical image to detect a medical condition from a list of medical conditions, wherein the list of medical conditions includes aortic dissection, pulmonary embolism, and coronary stenosis. Responsive to determining the medical image includes a first medical condition, the method generates a first report that includes information on a detection of the first medical condition. The method identifies, a medical specialist based on availability and medical expertise and sends to the identified medical specialist, the medical image and the first report for a decision on the detection of the first medical condition. Responsive to receiving the decision from the medical specialist, the method sends to a second electronic device, the decision, the medical image, and the first report.

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: 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: 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: Mechanisms are provided to implement a neural network, a concept extractor, and a machine learning model that operate to provide automatic contour annotation of medical images based on correlations with medical reports. The neural network processes a medical image to extract image features of the medical image. The concept extractor processes a portion of text associated with the medical image to extract concepts associated with the portion of text. The machine learning model correlates the extracted image features with the extracted concepts. An annotated medical image is generated based on the correlation of the extracted image features and extracted concepts. An annotation of the annotated medical image specifies a region of interest corresponding to both an extracted image feature and an extracted concept, thereby automatically mapping the portion of text to a relevant region of the medical image.

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

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 a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.

Abstract: Management of collections of medical documents is provided. In various embodiments, search criteria are specified for one or more datastore. Location information for a plurality of medical documents (e.g. images, textual documents, time series data, etc.) is retrieved from the one or more datastore. The location information for the plurality of medical documents is aggregated into a virtual collection. The virtual collection is indexed by metadata of the virtual collection.

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 medical episode analysis mechanism is provided. The mechanism analyzes patient information to extract a plurality of medical concept instances corresponding to medical characteristics associated with a patient. The mechanism generates a multi-dimensional time series data structure representing a clinical history timeline of the medical concept instances. The mechanism analyzes the multi-dimensional time series data structure to identify one or more medical episodes in the clinical history timeline, where a medical episode is a group of related encounters between one or more medical services providers and the patient. The mechanism outputs a visual representation of the identified one or more medical episodes to a computing device, where the visual representation provides a navigation user interface through which a user may browse the clinical history timeline associated with the patient.

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: According to one or more embodiments, a method, a computer program product, and a computer system for detecting and characterizing aortic pathologies are provided. The method may include receiving, by a computer, one or more tomograph scan images corresponding to a patient's aorta. The one or more received tomograph scan images may be analyzed by the computer for one or more image features associated with one or more aortic pathologies, such as aortic dissection or an aortic aneurysm. One or more image features associated with the one or more aortic pathologies may be identified in the one or more analyzed tomograph scan images, which may allow the determination of an aortic pathology associated with the patient's aorta based on the identification of the image features. A portion of the aorta and one or more branch arteries corresponding to the determined aortic pathology may then be identified.

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

Abstract: Mechanisms are provided to implement a generative adversarial network (GAN) that is trained based on labeled image data, unlabeled image data, and generated image data generated by a generator of the GAN. The GAN comprises a loss function that comprises error components for each of the labeled image data, unlabeled image data, and generated image data which is used to train the GAN. A new data source for which the trained GAN is to be adapted is identified and the trained GAN is adapted for the new data source. Image data in the new data source is classified by applying the adapted GAN to the data in the new data source. Adapting the trained GAN includes obtaining a minimized set of labeled images and utilizing the minimized set of images to perform the adapting of the trained GAN.

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: 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 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 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 a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.