Abstract: An apparatus comprises processing circuitry configured to: acquire convolutional neural network (CNN) layers; and train a first mapping layer which connects to an input layer of the CNN layers, and a second mapping layer which connects to an output layer of the CNN layers, wherein the first mapping layer maps omics input data to N-dimensional data, and wherein the second mapping layer receives further N-dimensional data that is output by the CNN layers and maps the further N-dimensional data to omics output data; wherein the training of the first mapping layer and the second mapping layer comprises fixing parameters of the CNN layers and minimizing a loss function, wherein the loss function is dependent on the input omics data and the output omics data.

Abstract: A medical information processing apparatus comprising: a memory; and processing circuitry configured to: receive omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules; calculate a respective distance between each pair of biomolecules from the plurality of biomolecules; apply a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules; adjust the positions to achieve a more even distribution of the positions over the two-dimensional space; and store, in the memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the adjusted positions.

Abstract: An apparatus includes processing circuitry configured to: obtain first trained parameters for a model, wherein the first trained parameters have been generated by training the model using data from a first data cohort; obtain second trained parameters for the model, wherein the second trained parameters have been generated by training the model using data from a second, different data cohort; determine a first evaluation value by inputting data from the first data cohort into a model having the first trained parameters; and determine a second evaluation value by inputting data from the first data cohort into a model having the second trained parameters.

Abstract: A system including processing circuitry configured to train a model for predicting from input data at least one predicted output, wherein the processing circuitry is configured to: receive a plurality of training data sets; receive from a user a selection of a first characteristic including positive and negative samples which are relevant variations significant to prediction of the at least one predicted output; receive from the user a selection of a second characteristic including an irrelevant sample which is a spurious variation irrelevant to the prediction of the predicted output; perform positive supervision of the model using the first characteristic such that the training of the model is sensitive to the positive and negative samples of the first characteristic; and perform negative supervision of the model using the second characteristic such that the training of the model is insensitive to the irrelevant sample of the second characteristic.

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: An apparatus comprises processing circuitry configured to: obtain first trained parameters for a model, wherein the first trained parameters have been generated by training the model using data from a first data cohort; obtain second trained parameters for the model, wherein the second trained parameters have been generated by training the model using data from a second, different data cohort; determine a first evaluation value by inputting data from the first data cohort into a model having the first trained parameters; and determine a second evaluation value by inputting data from the first data cohort into a model having the second trained parameters.

Abstract: A system comprises processing circuitry configured to perform a training method to train a model for predicting from input data at least one predicted output, the training method comprising: receiving a plurality of training data sets; receiving from a user an identification of a first characteristic of the training data sets as a positive characteristic which is relevant to prediction of the at least one predicted output; receiving from the user an identification of a second characteristic of the training data sets as a negative characteristic which is less relevant or irrelevant to prediction of the at least one predicted output; and training the model, the training of the model comprising: performing supervision of the model using the positive characteristic such that the model is trained to use values for the positive characteristic in the prediction of the at least one predicted output; and performing supervision of the model using the negative characteristic such that the model is trained to discount va

Abstract: An ultrasound diagnosis apparatus comprises processing circuitry configured to set initial values for a set of imaging parameters for use in acquiring ultrasound data for an ultrasound image, the set of imaging parameters comprising at least one acquisition parameter; acquire the ultrasound data according to the initial values for the set of imaging parameters, and process the ultrasound data to obtain the ultrasound image; extract imaging information in a region of interest of the ultrasound image; obtain predicted values for the set of imaging parameters using the extracted at least one feature and a machine learning algorithm trained using user-selected values for the set of imaging parameters, wherein the user-selected values are selected to provide a preferred appearance of the region of interest; and set the predicted values for the set of imaging parameters for use in acquiring further ultrasound data for a further ultrasound image.

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 a plurality of sets of medical imaging data; and train a classifier for use in classification, wherein the training of the classifier comprises, for each of the plurality of sets of medical imaging data: selecting a first part and a second part of the respective set of medical imaging data, wherein the first part and the second part are representative of different regions of the same subject; and training the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data.

Abstract: A medical image processing apparatus comprises a memory configured to store medical image data representative of a tissue structure and a processing circuitry configured to operationally connect to the memory, extract regions from the medical image data by performing threshold processing of the medical image data using each of a plurality of threshold values, select regions meeting at least one predetermined condition from among the extracted regions, and determine a region representative of the tissue structure in the medical image data based on the selected regions.

Abstract: A medical imaging data processing apparatus comprises a receiving unit configured to receive first medical imaging data that represents a region of a subject at a first time, and second medical imaging data that represents the region of the subject at a second, later time, a registration unit configured to perform a registration procedure to obtain registration data representative of a registration between the first medical imaging data and the second medical imaging data, an evaluation unit configured to process the registration data to determine, for each of a plurality of positions in the region, a value of a registration measure, the registration measure representing at least one feature of the registration as a function of position, wherein the evaluation unit is configured to, for at least some of the plurality of positions in the region, determine whether an abnormality is present by comparing the value of the registration measure to a statistical atlas.

Abstract: A medical image data processing apparatus comprises processing circuitry configured to: receive a plurality of sets of medical imaging data; and train a classifier for use in classification, wherein the training of the classifier comprises, for each of the plurality of sets of medical imaging data: selecting a first part and a second part of the respective set of medical imaging data, wherein the first part and the second part are representative of different regions of the same subject; and training the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data.

Abstract: An image data processing apparatus including a data receiver receiving image data to be segmented, and an atlas selection processor accessing a plurality of atlas data sets and selecting a subset of the atlas data sets for use in segmenting the image data, wherein the atlas selection processor is configured to select the subset of atlas data sets in dependence on the positions of one or more anatomical landmarks comprised in the plurality of atlas data sets.

Abstract: A medical image processing apparatus comprises a memory configured to store medical image data representative of a tissue structure and a processing circuitry configured to operationally connect to the memory, extract regions from the medical image data by performing threshold processing of the medical image data using each of a plurality of threshold values, select regions meeting at least one predetermined condition from among the extracted regions, and determine a region representative of the tissue structure in the medical image data based on the selected regions.

Abstract: A medical imaging data processing apparatus comprises a receiving unit configured to receive first medical imaging data that represents a region of a subject at a first time, and second medical imaging data that represents the region of the subject at a second, later time, a registration unit configured to perform a registration procedure to obtain registration data representative of a registration between the first medical imaging data and the second medical imaging data, an evaluation unit configured to process the registration data to determine, for each of a plurality of positions in the region, a value of a registration measure, the registration measure representing at least one feature of the registration as a function of position, wherein the evaluation unit is configured to, for at least some of the plurality of positions in the region, determine whether an abnormality is present by comparing the value of the registration measure to a statistical atlas.

Abstract: Certain embodiments provide a computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising: providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by: removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by: an optimization of the locations of the remaining landmarks by maximizing joint

Abstract: A medical image processing apparatus comprises an image data processing unit configured to determine a result by processing image data using an algorithm, a confidence interval (CI) evaluation unit configured to determine a confidence interval for the result from at least two initial results, and an output unit configured to provide the confidence interval to a user.

Abstract: An apparatus for locating a landmark in a set of image data comprises a landmark location unit that is configured, for each of a plurality of image data items, to obtain from a first two-class classifier a first classification of the image data item as foreground or background, to obtain from a second two-class classifier a second classification of the image data item as foreground or background, and to combine the first classification and the second classification to obtain a combined classification, and wherein the landmark location unit is further configured to use the combined classifications for the plurality of image data items to determine a location for the landmark.

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.