Abstract: A computer-implemented diagnostic-assistance system for medical applications, comprises: an artificial intelligence neural network configured to classify images of an obtained image dataset according to a set of classes; a confidence module configured to generate a confidence measure associated with each of the classified images; a tagging module configured to generate, for the patient, a diagnostic signal based on the generated confidence measures associated with the classified images, wherein the diagnostic signal for the patient is tagged as conclusive if a processed combination of the confidence measures fulfills a condition and tagged as inconclusive if the processed combination of the confidence measures does not fulfill the condition; and an output interface configured to output the diagnostic signal, wherein if the diagnostic signal is conclusive the classification result is released, and if the diagnostic signal is inconclusive an additional diagnostic analysis is triggered.

Abstract: Various examples of the disclosure pertain to using whole-slide images that depict healthy tissue for a training process for at least one machine-learning algorithm for digital pathology. For instance, an autoencoder neural network can be trained based on the healthy tissue.

Abstract: One or more example embodiments are methods and corresponding systems for providing a training data set for training a segmentation algorithm for segmenting whole-slide images in digital pathology as well as the use of the training data and corresponding ML segmentation algorithms. For example, a first segmentation of a whole slide image is refined based on an automatically generated annotation which has a higher level of detail than the first segmentation. A second segmentation results, which may be used as a ground truth for training the ML segmentation algorithm on the basis of the whole slide image.

Abstract: A set of pre-annotated medical images is received, and the received set is processed by automatically: training an AI-based uncertainty model using the received set as training data; processing medical images of the received set by determining classified segments and/or uncertainty regions in the medical images using the trained AI-based uncertainty model; selecting at least a part of the processed medical images including classified segments and/or uncertainty regions based on the processing result; and presenting the selected part of processed medical images to a human expert. Furthermore, a modified received set including additional annotations created by the human expert is received.

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: 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.