Abstract: A clinical decision support system performs a similarity search to determine the probable outcome of applying on a current patient those clinical actions that were performed on similar patients. The system analyzes stored electronic health records of similar patients so as to recommend diagnostic and therapeutic steps for the current patient. The system receives the health record of the patient, determines which clinical actions were already applied on the patient, generates classifiers associated with potential future clinical actions, generates a success value for each health record of another patient using the classifiers, displays the health record of the other patient having the greatest success value, and indicates a proposed clinical action that is to be applied on the patient. The system also calculates a quality value indicating the probability that a sequence of clinical actions that were applied to a similar patient will be successful if applied to the patient.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: A method of intuitively displaying values obtained from analyzing bio-medical images includes displaying a table of the values in a first pane of a graphical user interface. The table contains a user selectable row that includes a reference value and two numerical values. The reference value refers to an image of a tissue slice. The first numerical value is generated by performing image analysis on the image, and the second numerical value indicates a health state of the tissue. The image is displayed in a second pane of the graphical user interface in response to the user selecting the user selectable row. A graphical plot with a selectable symbol associated with the image is displayed in a third pane. The symbol has a position in the plot defined by the values. Alternatively, in response to the user selecting the selectable symbol, the image is displayed in the second pane.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: An analysis system analyzes digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers and object networks. Objects in a first object network are segmented into a first class, and objects in a second object network are segmented into a second class. One process step of the process hierarchy involves generating a third object network by imprinting objects of the first object network into the objects of the second object network such that pixel locations are unlinked from objects of the second object network to the extent that the pixel locations were also linked to objects of the first object network. The imprinting step allows object-oriented processing of digital images to be performed with fewer computations and less memory. Characteristics of an object of the third object network are then determined by measuring the object.

Abstract: A clinical decision support system performs a similarity search to determine the probable outcome of applying on a current patient those clinical actions that were performed on similar patients. The system analyzes stored electronic health records of similar patients so as to recommend diagnostic and therapeutic steps for the current patient. The system receives the health record of the patient, determines which clinical actions were already applied on the patient, generates classifiers associated with potential future clinical actions, generates a success value for each health record of another patient using the classifiers, displays the health record of the other patient having the greatest success value, and indicates a proposed clinical action that is to be applied on the patient. The system also calculates a quality value indicating the probability that a sequence of clinical actions that were applied to a similar patient will be successful if applied to the patient.

Abstract: A method of intuitively displaying values obtained from analyzing bio-medical images includes displaying a table of the values in a first pane of a graphical user interface. The table contains a user selectable row that includes a reference value and two numerical values. The reference value refers to an image of a tissue slice. The first numerical value is generated by performing image analysis on the image, and the second numerical value indicates a health state of the tissue. The image is displayed in a second pane of the graphical user interface in response to the user selecting the user selectable row. A graphical plot with a selectable symbol associated with the image is displayed in a third pane. The symbol has a position in the plot defined by the values. Alternatively, in response to the user selecting the selectable symbol, the image is displayed in the second pane.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.

Abstract: An analysis system analyzes digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers and object networks. Objects in a first object network are segmented into a first class, and objects in a second object network are segmented into a second class. One process step of the process hierarchy involves generating a third object network by imprinting objects of the first object network into the objects of the second object network such that pixel locations are unlinked from objects of the second object network to the extent that the pixel locations were also linked to objects of the first object network. The imprinting step allows object-oriented processing of digital images to be performed with fewer computations and less memory. Characteristics of an object of the third object network are then determined by measuring the object.

Abstract: A method for processing data structures with the aid of networked semantic units includes: acquiring a data structure, and generating, modifying, deleting and storing semantic structure units and networking them on the basis of the acquired data structure while using a knowledge base comprised of a network of semantic knowledge units. Semantic structure units and their network are classified in iterative steps. Based on this classification, a specific processing is activated which modifies a respective semantic structure unit and a particular partial network.

Abstract: A method is disclosed for generating an application that analyzes image data, such as from satellite and microscope pictures. The method uses a graphical user interface to add a new processing object to a processing object network. The processing object network includes a parent processing object and a child processing object. A user can append a new processing object to the child processing object or can add the new processing object as a subprocess to the parent processing object. The user selects a data domain and an algorithm from selection lists on the graphical user interface and adds them to the new processing object. The application uses a semantic cognition network to process data objects that are generated by segmenting the image data. The application then uses the new processing object to identify portions of the image that are to be highlighted on the graphical user interface.

Abstract: A method for processing data structures with the aid of networked semantic units. The method includes acquiring a data structure, and generating, modifying, deleting and/storing semantic structure units and/networking them on the basis of the acquired data structure. The method uses a knowledge base comprised of a network of semantic knowledge units. Semantic structure units and/their network are classified in iterative steps. Based on this classification, a specific processing is activated which modifies a respective semantic structure unit and/a particular partial network.

Abstract: In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.

Abstract: A method is disclosed for generating an application that analyzes image data, such as from satellite and microscope pictures. The method uses a graphical user interface to add a new processing object to a processing object network. The processing object network includes a parent processing object and a child processing object. A user can append a new processing object to the child processing object or can add the new processing object as a subprocess to the parent processing object. The user selects a data domain and an algorithm from selection lists on the graphical user interface and adds them to the new processing object. The application uses a semantic cognition network to process data objects that are generated by segmenting the image data. The application then uses the new processing object to identify portions of the image that are to be highlighted on the graphical user interface.

Abstract: A method is disclosed for extracting information from input data comprising mapping of the input data into a data object network. The method uses a semantic cognition network comprised of the data object network, a class object network and a processing object network. The semantic cognition network uses a set of algorithms to process the semantic units. The semantic cognition network defines a processing object in the processing object network by selecting a data domain in the data object network, a class domain in the class object network and an algorithm from the set of algorithms. The processing object comprises the data domain, the class domain and the algorithm. The processing object is used in the processing object network to process the semantic units.

Abstract: A method is disclosed for extracting information from input data comprising mapping of the input data into a data object network. The method uses a semantic cognition network comprised of the data object network, a class object network and a processing object network. The semantic cognition network uses a set of algorithms to process the semantic units. The semantic cognition network defines a processing object in the processing object network by selecting a data domain in the data object network, a class domain in the class object network and an algorithm from the set of algorithms. The processing object comprises the data domain, the class domain and the algorithm. The processing object is used in the processing object network to process the semantic units.