Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.

Abstract: Systems and methods for providing a means for improving the expressiveness and/or robustness of a machine learning system's result, based on imaging data and/or to make it possible to combine imaging data with non-imaging data to improve statements, which are deduced from the imaging data. The object is achieved by a computer implemented method, and uncertainty quantifier, medical system and a computer program product, and includes receiving a set of input data quantified as uncertainty, providing an information fusion algorithm, and applying the received set of input data on the provided information fusion algorithm, while modeling the propagation of uncertainty through the information fusion algorithm to predict an uncertainty for the medical assessment as a result (r), provided by the machine-learning system (M), based on the provided set of input data.

Abstract: For robust view classification and measurement estimation in sequential ultrasound imaging, the classification and/or measurements for a given image or sequence of images are gated. To prevent oscillation in results, the gating provides consistent output.

Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: Image segmentation of a structure of complex shape proceeds by acquiring by circuitry, a plurality of training images of the structure. A shape model of the structure is formed by fusing the plurality of training images. The shape model is partitioned by circuitry into a predetermined number of portions, an appearance and shape of each portion being determined by a classifier.

Abstract: Image segmentation of a structure of complex shape proceeds by acquiring by circuitry, a plurality of training images of the structure. A shape model of the structure is formed by fusing the plurality of training images. The shape model is partitioned by circuitry into a predetermined number of portions, an appearance and shape of each portion being determined by a classifier.