Abstract: In a method and a computer for determining a training function in order to generate annotated training images, a training image and training-image information are provided to a computer, together with an isolated item of image information that is independent of the training image. A first calculation is made in the computer by applying an image-information-processing first function to the isolated item of image information, and a second calculation is made by applying an image-information-processing second function to the training image. Adjustments to the first and second functions are made based on these calculation results, from which a determination of a training function is then made in the computer.

Abstract: A method and apparatus for mapping an acquisition protocol, for operating a medical imaging apparatus, to an acquisition protocol lexicon include extracting multiple tags from the acquisition protocol, performing text pre-processing in a computer on the extracted tags, converting the pre-processed text in the computer into an input feature set for a classifier, and applying the classifier to associate the input feature set with one or more entries of the acquisition protocol lexicon. The one or more entries of the acquisition protocol lexicon, with which the classifier associates the input feature set, are presented to a user as an output from the computer so as to inform a viewer (user) of those entries in the acquisition protocol lexicon that correspond to the input feature set.

Abstract: Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.

Abstract: A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.

Abstract: In order to compare high-dimensional, multi-modal data for a patient to data for other patients, deep learning is used to encode original, multi-modal data for a patient into a compact signature. The compact signature is compared to predetermined compact signatures generated for other patients, and similar predetermined compact signatures are identified based on the comparison. A clinical outcome may be predicted based on the similar predetermined compact signatures that are identified.

Abstract: Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.

Abstract: A first interface for reading a medical patient image record is provided. Furthermore, provision is made of an encoding module for machine-based learning of data encodings of image patterns by an unsupervised deep learning and for establishing a deep-learning-reduced data encoding of a patient image pattern contained in the patient image record. Furthermore, provision is made of a comparison module for comparing the established data encoding with reference encodings of reference image patterns stored in a database and for selecting a reference image pattern with a reference encoding which is similar to the established data encoding. An assignment module serves to establish a key term assigned to the selected reference image pattern and to assign the established key term to the patient image pattern. A second interface is provided for outputting the established key term with assignment to the patient image pattern.

Abstract: Using computational models for the patient physiology and the various therapy options, a decision support system presents a range of predicted outcomes to assist in planning the therapy. The models are used in various experiments for the many therapy options to determine an optimal approach.

Abstract: A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.

Abstract: Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.