Abstract: Systems and methods for detecting visual findings such as visual anomaly findings in computed tomography (CT) scans. The method includes: receiving a series of anatomical images obtained from a computed tomography (CT) scan of a head of a subject; generating, using the series of anatomical images by a preprocessing layer: a spatial 3D tensor which represents a 3D spatial model of the head of the subject; generating, using the spatial 3D tensor by a convolutional neural network (CNN) model: at least one 3D feature tensor; and classifying, using at least one of the 3D feature tensors by the CNN model: each of a plurality of possible visual anomaly findings as being present versus absent, the plurality of possible visual anomaly findings having a hierarchal relationship based on a hierarchical ontology tree.

Abstract: Computer implemented method for generating captions for medical images and/or clinical reports are provided. The methods comprise obtaining one or more medical images; using an image processing component to process the one or more images, wherein the image processing component comprises a deep learning model that takes as input the one or more medical images and produces as an output an image feature tensor; and using a natural language processing component to generate a caption for the one or more medical images, wherein the natural language processing component comprises a transformer-based model that takes as input the image feature tensor from the image processing component and produces as output a probability for each word in a vocabulary. Related systems and products are also described.

Abstract: This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.

Abstract: This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.