Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Automatic discrepancy detection in medical data is provided. In various embodiments, a disease label for a present study is determined, indicative of a disease condition. Retrospective review of a plurality of electronic medical records is performed. The retrospective review comprises searching for electronic medical records relevant to the disease condition. The earliest electronic medical record reflecting the disease condition is identified. Based on the earliest electronic medical record reflecting the disease condition, one or more of the electronic medical records having an omission or inconsistency is identified. The one or more of the electronic medical records having an omission or inconsistency are flagged for supplemental review in a worklist.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Workflows for automatic measurement of Doppler is provided. In various embodiments, a plurality of frames of a medical video are read. A mode label indicative of a mode of each of the plurality of frames is determined. At least one of the plurality of frames is provided to a trained feature generator. The at least one of the plurality of frames have the same mode label. At least one feature vector is obtained from the trained feature generator corresponding to the at least one of the plurality of frames. At least one feature vector is provided to a trained classifier. A valve label indicative of a valve is obtained from the trained classifier corresponding to the at least one of the plurality of frames. One or more measurement is extracted indicative of a disease condition from those of the at least one of the plurality of frames matching a predetermined valve label.

Abstract: Automatic detection of disease presence from combining disease-specific measurements with textual descriptions of disease and its severity in unstructured textual reports is provided. In various embodiments, a knowledge graph of clinical concepts is read. Based on the knowledge graph, a plurality of associations are determined between disease names, symptoms, anatomical abnormalities, and qualifiers. A corpus of clinical reports is read. Based on the plurality of associations, a plurality of portions indicative of a disease condition are located within the corpus of clinical reports. Within each of the plurality of portions, name/value pairs are detected corresponding to measurements indicative of the disease condition. The measurements indicative of the disease condition are extracted.

Abstract: A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

Abstract: A generalized search engine is provided for abstract data types with skimming and approximate retrieval. According to various embodiments, an abstract data type definition is generated. The abstract data type definition comprises a plurality of field-value pairs. The abstract data type definition is indexed in an index. A document definition is generated. The document definition comprises at least one key value, and further comprises the abstract data type definition. The document definition is indexed in the index.

Abstract: Embodiments of the present invention relate to discriminating between normal and abnormal left ventricles in echocardiography. In some embodiments, a first region of a first image of a heart is located by clustering. The first region depicts a chamber of the heart. The chamber has a chamber shape. A boundary of the first region is determined by contour tracing a retaining the traced contour overlapping the first region. A predetermined shape is compared to the boundary. The predetermined shape had been determined without reference to the first image. A first plurality of parameters is determined that, when applied to the predetermined shape, conforms the predetermined shape to the boundary and minimizes at least one error between the predetermined shape and the boundary. An indication of normality or abnormality is determine from the first plurality of parameters.

Abstract: A generalized distributed framework is provided for parallel search and retrieval of unstructured and structured patient data across zones with hierarchical ranking. In various embodiments, patient data is ingested from a plurality of data sources. A plurality of data models is populated based on the ingested patient data, each data model comprising an abstract data type. The plurality of data models is stored in an index. A search request is processed against the index, the search request comprising one or more attribute of the abstract data type.

Abstract: Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos is provided. In various embodiments, a plurality of medical images containing embedded text are read. The plurality of medical images are clustered into a plurality of clusters. Medical images of a first cluster of the plurality of clusters are ranked based on the frequency of measurement names within the medical images of the first cluster. A candidate tabular template is generated indicative of a layout of measurement name/value pairs within the medical images of the first cluster. According to the candidate tabular template, optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements. The candidate tabular template and the candidate measurements are presented to a user.

Abstract: Automatic detection of valve disease from analysis of Doppler waveforms exploiting the echocardiography annotations is provided. In various embodiments, a frame is selected from a medical video. The selected frame depicts a valve of interest. A Doppler envelope is extracted from the selected frame. Based on the frame and the Doppler envelope, one or more measurements indicative of a disease condition are extracted.

Abstract: Sequential learning techniques, such as auto-context, that apply the output of an intermediate classifier as contextual features for its subsequent classifier have shown impressive performance for semantic segmentation. It is shown that these methods can be interpreted as an approximation technique derived from a Bayesian formulation. To improve the effectiveness of applying this approximation technique, a new sequential learning approach is proposed for semantic segmentation that solves a segmentation problem by breaking it into a series of simplified segmentation problems. Sequentially solving each of the simplified problems along the path leads to a more effective way for solving the original segmentation problem. To achieve this goal, a learning-based method is proposed to generate simplified segmentation problems by explicitly controlling the complexities of the modeling classifiers. Promising results were reported on the 2013 SATA canine leg muscle segmentation dataset.

Abstract: Registration-based interpolation between images slices is provided. In various embodiments, a plurality of 2D images is read. Each of the plurality of 2D images represents a slice of a 3D volume. A plurality of annotations is read for a subset of the plurality of 2D images. The annotations comprise at least one anatomical label. 2D images lacking annotations are selected from the plurality of 2D images. The at least one anatomical label is propagated from the subset of the plurality of 2D images to the selected 2D images by deformable registration.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: A generalized search engine is provided for abstract data types with skimming and approximate retrieval. According to various embodiments, an abstract data type definition is generated. The abstract data type definition comprises a plurality of field-value pairs. The abstract data type definition is indexed in an index. A document definition is generated. The document definition comprises at least one key value, and further comprises the abstract data type definition. The document definition is indexed in the index.

Abstract: In various embodiments, extraction of clinical concepts from large clinical report collections is provided. A plurality of reports is read from a report collection. Each of the plurality of reports is divided into constituent phrases. A first plurality of prefixes is extracted from the constituent phrases of the plurality of reports. An index is created based on the first plurality of prefixes. A second plurality of prefixes is read. The second plurality of prefixes corresponds to a clinical concept. From the index, a subset of the plurality of reports is determined that are relevant to the clinical concept.

Abstract: Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Abstract: Registration-based interpolation between images slices is provided. In various embodiments, a plurality of 2D images is read. Each of the plurality of 2D images represents a slice of a 3D volume. A plurality of annotations is read for a subset of the plurality of 2D images. The annotations comprise at least one anatomical label. 2D images lacking annotations are selected from the plurality of 2D images. The at least one anatomical label is propagated from the subset of the plurality of 2D images to the selected 2D images by deformable registration.

Abstract: Cohort detection from multimodal data by machine learning is provided. In various embodiments, a plurality of patient records associated with a patient are read from a plurality of data sources. A plurality of disease-specific features are extracted from the plurality of patient records. The plurality of disease-specific features are provided to a classifier. An indicator of a likely disease condition of the patient is received from the classifier.

Abstract: Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos is provided. In various embodiments, a plurality of medical images containing embedded text are read. The plurality of medical images are clustered into a plurality of clusters. Medical images of a first cluster of the plurality of clusters are ranked based on the frequency of measurement names within the medical images of the first cluster. A candidate tabular template is generated indicative of a layout of measurement name/value pairs within the medical images of the first cluster. According to the candidate tabular template, optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements. The candidate tabular template and the candidate measurements are presented to a user.