Abstract: A framework for processing medical images. The framework may include receiving a target medical image, a reference medical image and at least one marker associated with a location in the reference medical image. A corresponding location of the at least one marker is determined in the target medical image. The target medical image is overlaid with the at least one marker at the determined corresponding location to provide an overlaid image. Display data is generated to cause a display device to display the overlaid image.

Abstract: A framework for identifying a type of organ in a volumetric medical image. The framework may include receiving a volumetric medical image, the volumetric medical image comprising at least one organ or portion thereof, and further receiving a single point of interest within the volumetric medical image. Voxels are sampled from the volumetric medical image, wherein at least one voxel is skipped between two sampled voxels. The type of organ is identified at the single point of interest by applying a trained classifier to the sampled voxels.

Abstract: A computer-implemented method of encoding a word for use in a method of text analysis comprises receiving input text to be analysed, the input text comprising a first word which is not represented in a vocabulary set stored on a storage. The vocabulary set comprises a plurality of words and an associated word embedding vector for each word in the set. The method comprises identifying the first word as a word which is not represented in the vocabulary set and determining one or more sub-words within the first word with which to encode the first word. Each of the one or more sub-words corresponds with a word represented in the vocabulary set and having an embedding vector in the vocabulary set. The method comprises determining an encoding for the first word based on the one or more sub-words.

Abstract: A method, device and system for automated processing of medical images to output alerts for detected dissimilarities in the medical images is provided. In one aspect, the method comprises receiving a first medical image of an anatomical object of a patient, the first medical image being acquired at a first instance of time; receiving a second medical image of the anatomical object of the patient, the second medical image being acquired at a second instance of time; determining an image similarity between image data of the first medical image and image data of the second medical image; determining a dissimilarity between the first medical image and the second medical image based on the image similarity; and outputting an alert for the dissimilarity.

Abstract: A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.

Abstract: There is disclosed a method and apparatus for annotating a first portion of medical imaging data with one or more words corresponding to a respective one or more features represented in the first portion of medical imaging data. A similarity metric indicating a degree of similarity between the first portion and each of a plurality of second portions of reference medical imaging data is determined, at least one of the plurality of second portions being annotated with one or more first words corresponding to a respective one or more features represented in the second portion. A second portion is selected based on the similarity metrics, and the first portion is annotated with the one or more first words with which the second portion, selected for the first portion, is annotated.

Abstract: A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.

Abstract: Computer-implemented methods and systems for identifying corresponding slices in medical image data sets are provided. For example, the systems and methods are based on identifying corresponding slices by systematically quantifying image similarities between the slices comprised in one medical image data set and the slices comprised in another medical image data set.

Abstract: A framework for segmenting a medical text report into sections is disclosed. For each sentence of the report, a first sentence representation is determined by inputting a word-level context representation for each sentence sequentially into a neural network. A second sentence representation is determined by inputting an aggregated representation for each sentence sequentially into another neural network. For each sentence, a third sentence representation is determined based on a combination of the first and second sentence representations, and a section classification for the sentence is determined by inputting the third sentence representation into a section classifier. Each sentence is assigned the section classification determined for the sentence.

Abstract: A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

Abstract: A computer-implemented method is for clinical decision support. In an embodiment, the method includes receiving patient data of a target patient; determining, based on the patient data, a number of potential clinical outcomes associated with the target patient; calculating, for each respective potential clinical outcome, a respective probability of being indicated by the patient data; selecting, based on the plurality of probabilities calculated, one or more anamnestic questions from a set of anamnestic questions stored; presenting the one or more anamnestic questions selected to a user via a user interface; receiving one or more answers to the one or more anamnestic questions selected, from the user; and adapting the plurality of probabilities based upon the one or more answers received.

Abstract: Method and apparatus for image retrieval. An image descriptor of a query image indicating a medical abnormality and non-image patient data may be received. For each of a plurality of candidate images stored in a database, an image descriptor of the candidate image and data about a medical abnormality known to be indicated by the candidate image may further be received. A similarity metric between the image descriptors of the query image and the candidate image may be determined for each candidate image. A first probability of the query image medical abnormality being the candidate image medical abnormality given the non-image patient data associated with the query image may be determined for each candidate image. A score may then be determined for each candidate image based on the determined similarity metric and the determined first probability. One or more of the candidate images may be retrieved from the database in accordance with the determined scores.

Abstract: A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.

Abstract: A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

Abstract: A computer-implemented method of encoding a word for use in a method of text analysis comprises receiving input text to be analysed, the input text comprising a first word which is not represented in a vocabulary set stored on a storage. The vocabulary set comprises a plurality of words and an associated word embedding vector for each word in the set. The method comprises identifying the first word as a word which is not represented in the vocabulary set and determining one or more sub-words within the first word with which to encode the first word. Each of the one or more sub-words corresponds with a word represented in the vocabulary set and having an embedding vector in the vocabulary set. The method comprises determining an encoding for the first word based on the one or more sub-words.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A framework for automatic retrieval of medical images. In accordance with one aspect, the framework detects patches in a query image volume that contain at least a portion of an anatomical region of interest by using a first trained classifier. The framework determines disease probabilities by applying a second trained classifier to the detected patches, and selects, from the patches, a sub-set of informative patches with disease probabilities above a pre-determined threshold value. For a given patch from the sub-set of informative patches, the framework retrieves, from a database, patches that are most similar to the given image. Image volumes associated with the retrieved patches are then retrieved from the database. A report based on the retrieved image volumes may then be generated and presented.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.

Abstract: A framework for automatic retrieval of medical images. In accordance with one aspect, the framework detects patches in a query image volume that contain at least a portion of an anatomical region of interest by using a first trained classifier. The framework determines disease probabilities by applying a second trained classifier to the detected patches, and selects, from the patches, a sub-set of informative patches with disease probabilities above a pre-determined threshold value. For a given patch from the sub-set of informative patches, the framework retrieves, from a database, patches that are most similar to the given image. Image volumes associated with the retrieved patches are then retrieved from the database. A report based on the retrieved image volumes may then be generated and presented.