Abstract: A data processing apparatus configured to train an image analysis model to perform a task relating to input data which includes at least image data, wherein the data processing apparatus comprises processing circuitry configured to: receive the input data; generate image tokens for inputting to the image analysis model by applying a visual extractor model that is trained to identify an anatomical region included in the image data which is comprised in the input data and to determine a label relating to a pre-determined sub-task relating to said anatomical region; and train the image analysis model by inputting at least the image tokens.

Abstract: A medical image processing apparatus comprises processing circuitry configured to: receive medical image data for processing; receive text data including clinical information relating to the medical image data; specify based on the text data a target processing region of the medical image data or of a space. derived from the medical image data; and process at least the specified target processing region of the medical image data or of a space derived from the medical image data.

Abstract: A medical information processing system for processing medical data relating to a target patient comprises a memory storing a knowledge base, wherein the knowledge base is based on medical knowledge; and processing circuitry configured to receive trigger information; and determine a necessity of alerting relating to the trigger information based on the trigger information and the knowledge base.

Abstract: An apparatus for medical text processing comprises processing circuitry configured to: obtain a trained model, wherein the trained model is trained to classify medical text documents with a medical classification code; apply the trained model to at least one medical text document to obtain weightings for text terms included in the at least one medical text document, wherein the weightings are associated with the medical classification code; and use the weightings to perform a searching or indexing process.

Abstract: An apparatus for training a model to identify abnormal medical/image data comprises processing circuitry configured to: receive medical/image data; obtain a local region and a context region from the medical/image data; generate abnormal medical/image data using the local region and/or the context region; train a model using the medical/image data and the generated abnormal medical/image data to identify abnormal medical/image data.

Abstract: A medical image data processing apparatus is provided and includes processing circuitry to receive medical image data in respect of at least one subject; receive non-image data; generate a filter based on the non-image data; and apply the filter to the medical image data, wherein the filter limits a region of the medical image data.

Abstract: A medical information processing apparatus comprises: a memory which stores a plurality of semantic ranking values for a plurality of medical terms, wherein each of the semantic ranking values relates to a degree of semantic similarity between a respective pair of the medical terms; and processing circuitry configured to train a model based on the semantic ranking values, wherein the model comprises a respective vector representation for each of the medical terms.

Abstract: A medical system comprises processing circuitry configured to: receive a first trained model, wherein the trained model has been trained using a first data set acquired in a first cohort; receive a second data set acquired in a second cohort; input data included in the second data set and data representative of the first trained model into a second trained model; and receive from the second trained model an affinity-relating value which represents an affinity between the data included in the second data set and the first trained model.

Abstract: A medical information processing apparatus is for processing medical text data using a medical text processing model. The apparatus comprising processing circuitry configured to: receive a portion of medical text data; determine a template corresponding to the portion of medical text data, a classification label associated with the template, and a first medical term included in the portion of medical text data; specify a second medical term which is different from the first medical term, wherein the second medical term is related to the first medical term; and generate synthetic text data based on the second term by inserting the second medical term into the template.

Abstract: A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.

Abstract: An image data processing apparatus comprises processing circuitry configured to: receive first medical image data; obtain a first image feature data set from the first medical image data; obtain a second image feature data set from second medical image data and/or from stored image feature data; obtain style data; and generate synthesized image data based on the first image feature data set, the second image feature data set, and the style data.

Abstract: An apparatus for medical text processing comprises processing circuitry configured to: obtain a trained model, wherein the trained model is trained to classify medical text documents with a medical classification code; apply the trained model to at least one medical text document to obtain weightings for text terms included in the at least one medical text document, wherein the weightings are associated with the medical classification code; and use the weightings to perform a searching or indexing process.

Abstract: A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.

Abstract: An apparatus for determining similarity between medical data sets for a plurality of patients or other subjects comprises at least one data store and a processing resource. The at least one data store is configured to store a respective representation of each of a plurality of data sets, the representation of each data set being generated by applying a model for representing data sets with respect to a plurality of features. The processing resource is configured to use the model to obtain a representation of a further medical data set, and perform a similarity determining process to determine similarity between the representation of the further medical data set and at least some of said representations of said plurality of medical data sets.

Abstract: A medical image data processing apparatus comprises processing circuitry configured to: receive medical image data in respect of at least one subject; receive non-image data; generate a filter based on the non-image data; and apply the filter to the medical image data, wherein the filter is configured to limit a region of the medical image data.

Abstract: A medical system comprises processing circuitry configured to: receive a first trained model, wherein the trained model has been trained using a first data set acquired in a first cohort; receive a second data set acquired in a second cohort; input data included in the second data set and data representative of the first trained model into a second trained model; and receive from the second trained model an affinity-relating value which represents an affinity between the data included in the second data set and the first trained model.

Abstract: An apparatus for determining similarity between medical data sets for a plurality of patients or other subjects comprises at least one data store and a processing resource. The at least one data store is configured to store a respective representation of each of a plurality of data sets, the representation of each data set being generated by applying a model for representing data sets with respect to a plurality of features. The processing resource is configured to use the model to obtain a representation of a further medical data set, and perform a similarity determining process to determine similarity between the representation of the further medical data set and at least some of said representations of said plurality of medical data sets.

Abstract: A medical image processing apparatus, comprises a landmark identification unit configured to process a medical image data set to identify a landmark corresponding to a part of a branching structure according to a stored anatomical representation, and a spatial analysis unit configured to process the image data set to determine a spatial configuration of at least part of the branching structure.

Abstract: A medical image processing apparatus, comprises a landmark identification unit configured to process a medical image data set to identify a landmark corresponding to a part of a branching structure according to a stored anatomical representation, and a spatial analysis unit configured to process the image data set to determine a spatial configuration of at least part of the branching structure.