Abstract: The present disclosure is directed to an apparatus and method for data analysis for use in data classification via training of a recurrent neural network to identify features from limited reference sets. Based on a one-shot learning algorithm, the method includes selecting a subset of reference data and training a classifier with the selected data. This small subset of reference data can be iteratively tuned to enhance classification of the data according to the desired output of the method. The apparatus may be configured to allow a user to interactively select a subset of reference data which is used to train the classifier and to evaluate classifier performance.

Abstract: The present disclosure is directed to an apparatus and method for data analysis for use in data classification via training of a recurrent neural network to identify features from limited reference sets. Based on a one-shot learning algorithm, the method includes selecting a subset of reference data and training a classifier with the selected data. This small subset of reference data can be iteratively tuned to enhance classification of the data according to the desired output of the method. The apparatus may be configured to allow a user to interactively select a subset of reference data which is used to train the classifier and to evaluate classifier performance.

Abstract: The present disclosure is directed to an apparatus and method for data analysis for use in data classification via training of a recurrent neural network to identify features from limited reference sets. Based on a one-shot learning algorithm, the method includes selecting a subset of reference data and training a classifier with the selected data. This small subset of reference data can be iteratively tuned to enhance classification of the data according to the desired output of the method. The apparatus may be configured to allow a user to interactively select a subset of reference data which is used to train the classifier and to evaluate classifier performance.

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 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 comprises processing circuitry configured to receive a first model and a second model; determine difference information that is representative of a difference between the first model and the second model and/or between the first task and the second task and/or between the first domain and the second domain; and generate a third model using the first model, the second model and the difference information, wherein the generating of the third model comprises training the third model to perform both of the first task and the second task and/or to operate on both the first domain and the second domain.

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: 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 data processing apparatus for training models on data, comprises processing circuitry configured to: train a first model on a plurality of labelled data sets; apply the first trained model to a plurality of non-labelled data sets to obtain first pseudo-labels; train a second model using at least the labelled data sets, the non-labelled data sets and the first pseudo-labels; apply the second trained model to non-labelled data sets to obtain second pseudo-labels; and train a third model based on at least the labelled data sets, non-labelled data sets and the second pseudo-labels.

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: The present disclosure is directed to an apparatus and method for data analysis for use in data classification via training of a recurrent neural network to identify features from limited reference sets. Based on a one-shot learning algorithm, the method includes selecting a subset of reference data and training a classifier with the selected data. This small subset of reference data can be iteratively tuned to enhance classification of the data according to the desired output of the method. The apparatus may be configured to allow a user to interactively select a subset of reference data which is used to train the classifier and to evaluate classifier performance.

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 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.