Multi-modal medical image processing
AssigneeKheiron Medical Technologies Ltd
InventorsPETER KECSKEMETHY, TOBIAS RIJKEN
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Abstract
The present invention relates to the identification of regions of interest in medical images. More particularly, the present invention relates to the identification of regions of interest in medical images based on encoding and/or classification methods trained on multiple types of medical imaging data. Aspects and/or embodiments seek to provide a method for training an encoder and/or classifier based on multimodal data inputs in order to classify regions of interest in medical images based on a single modality of data input source.
Claims28
A method for automatically identifying regions of interest in medical or clinical image data, the method comprising:
- receiving unlabelled input data, the input data comprising data from one of a plurality of modalities of data;
- encoding the unlabelled input data using a trained encoder;
- determining a joint representation using a trained joint representation module;
- and
- generating labelled data for the input data by using the joint representation as an input for a trained classifier.
A method of training a classifier for medical or clinical data, the method comprising:
- receiving unlabelled input data from a pre-labelled data set, the input data comprising data from a plurality of modalities;
- encoding the unlabelled input data from a plurality of modalities to form a joint representation;
- performing classification using an adaptable classification algorithm on the joint representation to generate labelled data from the joint representation;
- comparing pre-labelled data from the pre-labelled data set to the labelled data and outputting comparison data;
- adjusting the adaptable classification algorithm in response to the comparison data;
- and
- repeating the method until the comparison data has reached a predetermined threshold indicating that no further adjustments need to be made to the adaptable classification algorithm.
A method of classifying data for medical or clinical purposes, comprising:
- receiving unlabelled input data, the input data comprising data from one of a plurality of modalities of data;
- encoding the unlabelled input data into a trained joint representation using a trained encoder;
- and
- performing classification using a classification algorithm on the trained joint representation to generate labelled data from the trained joint representation.
(canceled)
Dependencies are inferred from the claim text.