Abstract: A method for detection and characterization of lesions includes acquiring a plurality of phase images of a multi-phase imaging exam, extracting a local context for each phase image of the plurality of phase images, encoding the local contexts to create phase specific feature maps, combining the phase-specific feature maps to create unified feature maps, and at least one of characterizing or detecting a lesion based on the unified feature maps

Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Abstract: A method of and an Artificial Intelligence (AI) system for predicting hemodynamic parameters for a target vessel, in particular of an aorta, as well as to a computer-implemented method of training an AI unit of the AI system are disclosed. A vessel shape model of the target vessel and a corresponding flow profile of the target vessel are received. At least one hemodynamic parameter is predicted by the AI unit based on the received vessel shape model and the received flow profile. The AI unit is arranged and configured to predict at least one hemodynamic parameter based on a received vessel shape model and a received flow profile of the target vessel (aorta).

Abstract: For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.

Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Abstract: Various disclosed examples pertain to digital pathology, more specifically to training of a segmentation algorithm for segmenting whole-slide images depicting tissue of multiple types. An initial annotation of a whole-slide image is refined to yield a refined annotation based on which parameters of the segmentation algorithm can be set. Techniques of patch-wise weak supervision can be employed for such refinement.

Abstract: One or more example embodiments of the present invention is based on a computer-implemented method for providing molecular data. The method comprises receiving a computed tomography image of at least a part of a lung of a patient, wherein the computed tomography image depicts at least one lung nodule. The molecular data is determined by processing first input data with a first trained function, wherein the first input data is based on the computed tomography image, and wherein the molecular data relates to a biomarker within at least one of a genome of the patient, a transcriptome of the patient, a proteome of the patient or a metabolome of the patient. Furthermore, the molecular data is provided. Providing the molecular data can comprise at least one of displaying, transmitting or storing the molecular data.

Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Abstract: Systems and methods for generating synthesized medical images for training a machine learning based network. An input medical image in a first modality is received comprising a nodule region for each of one or more nodules, a remaining region and an annotation for each of the nodules. A synthesized medical image in a second modality is generated from the input medical image comprising the annotation for each of the nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether a particular nodule is visible in the synthesized medical image based on the synthesized nodule image for the particular nodule and the synthesized remaining image. If at least one nodule is not visible in the synthesized medical image, the annotation for the not visible nodule is removed from the synthesized nodule image.

Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Abstract: For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.

Abstract: Systems and methods for generating synthesized medical images for training a machine learning based network are provided. An input medical image in a first modality is received. The input medical image comprises a nodule region for each of one or more nodules and a remaining region. The input medical image comprises an annotation for each of the one or more nodules. A synthesized medical image in a second modality is generated from the input medical image. The synthesized medical image comprises the annotation for each of the one or more nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether each particular nodule of the one or more nodules is visible in the synthesized medical image based on at least one of the synthesized nodule image for the particular nodule and the synthesized remaining image.

Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Abstract: A method is for generating modified medical images. An embodiment of the method includes receiving a first medical image displaying an abnormal structure within a patient, and applying a trained inpainting function to the first medical image to generate a modified first medical image, the trained inpainting function being trained to inpaint abnormal structures within a medical image. The method includes determining an abnormality patch based on the first medical image and the modified first medical image; receiving a second medical image of the same type as the first medical image; and including the abnormality patch into the second medical image to generate a modified second medical image. A method is for detecting abnormal structures using a trained detection function trained based on modified second medical images. Systems, computer programs and computer-readable media related to those methods are also disclosed.

Abstract: Systems and methods are provided for generating a synthesized medical image patch of a nodule. An initial medical image patch and a class label associated with a nodule to be synthesized are received. The initial medical image patch has a masked portion and an unmasked portion. A synthesized medical image patch is generated using a trained generative adversarial network. The synthesized medical image patch includes the unmasked portion of the initial medical image patch and a synthesized nodule replacing the masked portion of the initial medical image patch. The synthesized nodule is synthesized according to the class label. The synthesized medical image patch is output.

Abstract: Systems and methods for generating synthesized images are provided. An input medical image patch, a segmentation mask, a vector of appearance related parameters, and manipulable properties are received. A synthesized medical image patch including a synthesized nodule is generated based on the input medical image patch, the segmentation mask, the vector of appearance related parameters, and the manipulable properties using a trained object synthesis network. The synthesized nodule is synthesized according to the manipulable properties. The synthesized medical image patch is output.

Abstract: Systems and methods for generating synthesized images are provided. An input medical image patch, a segmentation mask, a vector of appearance related parameters, and manipulable properties are received. A synthesized medical image patch including a synthesized nodule is generated based on the input medical image patch, the segmentation mask, the vector of appearance related parameters, and the manipulable properties using a trained object synthesis network. The synthesized nodule is synthesized according to the manipulable properties. The synthesized medical image patch is output.

Abstract: Systems and methods are provided for generating a synthesized medical image patch of a nodule. An initial medical image patch and a class label associated with a nodule to be synthesized are received. The initial medical image patch has a masked portion and an unmasked portion. A synthesized medical image patch is generated using a trained generative adversarial network. The synthesized medical image patch includes the unmasked portion of the initial medical image patch and a synthesized nodule replacing the masked portion of the initial medical image patch. The synthesized nodule is synthesized according to the class label. The synthesized medical image patch is output.

Abstract: A method of and an Artificial Intelligence (AI) system for predicting hemodynamic parameters for a target vessel, in particular of an aorta, as well as to a computer-implemented method of training an AI unit of the AI system are disclosed. A vessel shape model of the target vessel and a corresponding flow profile of the target vessel are received. At least one hemodynamic parameter is predicted by the AI unit based on the received vessel shape model and the received flow profile. The AI unit is arranged and configured to predict at least one hemodynamic parameter based on a received vessel shape model and a received flow profile of the target vessel (aorta).