Abstract: A computer-implemented method of enhancing object detection in a digital image of known underlying structure using pre-processed images with underlying structure and with any objects detected and bounding boxes inserted over the objects, the method comprising: extracting or generating images with the underlying structure but without objects detected as negative images; extracting images with the underlying structure and with an object detected as positive images; inputting pairs of negative and positive images through a feature extraction section in a neural network to extract feature vectors of the images; contrasting feature vectors of each pair of positive and negative images and thereby provide a contrast vector and gating the result to form an attention vector; processing the attention vector and the feature vector of the positive image to produce an output.

Abstract: A computer-implemented method of active learning for computer vision in digital images, comprising: inputting labelled image training examples into an artificial neural network in a training phase; training a computer vision model using the labelled training examples; carrying out a prediction task on each image of an unlabelled training set of unlabelled, unseen images using the model; calculating an uncertainty metric for the predictions in each image of the unlabelled training set; calculating a similarity metric for the unlabelled training set representing similarities between the images in the training set; selecting images from the unlabelled training set, in dependence upon both the similarity metric and the uncertainty metric of each image, to design a training set for labelling which tends to both lower the similarity between the selected images and increase the uncertainty of the selected images.

Abstract: Systems and methods are described for automatically determining layer structure from medical image data. A processing device receives image data of biological layers captured by a medical imaging device. The processing device determines a boundary surface score for each pixel of the image data using a neural network, the boundary surface score being representative of a likelihood that each pixel corresponds to a boundary between segmented layers within the image data, to generate data defining boundary surfaces between segmented layers in the image data. In one embodiment, the neural network includes first and second sub-networks connected in series, the first sub-network configured with a multi-scale pooling layer that provides additional filters at respective defined sampling rates.