Abstract:
A computer-implemented method for developing a mammography deep learning model wherein a set of task-specific mammography deep learning models is developed, each trained for performing a different task on a mammography dataset and each generating one or more feature vectors and wherein the task-specific models are combined to a patient model to obtain a patient prediction by fusing said feature vectors.
Abstract:
A first visual image of a region of interest is generated by means of an optical camera at the time when a first radiation image is recorded, a second visual image is generated by means of an optical camera of said same region of interest, first and second visual images are compared, and a second radiation image is recorded when said first and second visual images are substantially identical.
Abstract:
A method of augmenting the number of labeled images for training a neural network comprising the steps of - Starting from a dataset of labeled images with corresponding segmentation masks and a dataset of unlabeled images, gathering for a given image i in a data set of labeled images a number of images with similar metadata in said dataset of unlabeled images so as to form data sub-set Sim i, - Training a multiclass segmentation neural network on said labeled images thereby generating segmentation masks for the images in subset Sim i, - On the basis of these segmentation masks judging similarity between images of Sim i and image i and finding the most similar image(s) in Sim i by computing and comparing histograms of segmentation masks of image i and images in Sim i - Transferring the histogram of the most similar images in Sim i to given image i.
Abstract:
Systems and methods for integrating a plurality of healthcare software applications. A user initiates one or more software applications required to perform different parts of a healthcare task. The software applications include a local application installed on the user's device and a web-based application accessed through a browser. A host application is launched in order to facilitate local two-way communication between the local application and the web-based application. The host application generates a local communication server on the user device through which the local application and web application communicate. This allows a user to easily and efficiently perform multiple parts of the healthcare tasks. This also allows different healthcare software applications to be integrated without requiring any plug-ins or other integration components to be installed on the user's device.
Abstract:
A phase contrast imaging (PCI) method in which instead of using an analyzer grid detector pixels are grouped and only a part of the total pixels are used to calculate a phase contrast image. In a second, third... step the pixels which were not used in the previous recalculation are used additionally to recalculate a second, third.... phase contrast image. Finally the different phase contrast images are fused to result in a full image.
Abstract:
On the basis of user input a set of contour points of the 3D object is detected in a number of 2D slice images representing the 3D object. Next a 2D object is segmented in each of the slice images by means of the set of contour points so as to obtain segmentation masks. Finally by means of interpolation between computed segmentation masks, the 3D object is segmented.
Abstract:
Method and system to fully-automatically analyze a medical image represented by a digital image representation. A method is disclosed to automatically detect systemic arteries in arbitrary field-of-view computed tomography angiography (CTA).
Abstract:
An adaptive reconstruction kernel taking up to four main and diagonal, defect-free sub-kernel directions into account is composed for each defective image pixel. The defective pixels impacted image is real-time corrected by statistical filtering or by a weighed directional convolution of kernel-associated replacement values, calculated by means of an advanced multi-parabolic reconstruction algorithm, for each contributing sub-kernel direction based on 5 x 5 pixels neighborhood image data readily accessible via a predetermined AMP kernels image-offsets structure.
Abstract:
A depth profile of a region of interest in a 3D image of a biological tissue obtained through full field time domain high definition optical coherence tomography imaging is subjected to curve fitting. Parameters linked to optical anisotropy characteristics of this tissue are deduced from fitted curves and are compared to set values of these parameters, the set values being indicative of normal, precancerous or cancerous tissue characteristics.
Abstract:
A method for reducing image disturbances caused by reconstructed defective pixel clusters located in signal-gradient affected diagnostic image regions. An individually adapted central symmetrical pair reconstruction (CSP) kernel is composed for a defective image pixel based on a kernel-pair candidate order encoded in a model thereby using the pixel's validity state. The image impacted by defective pixels is corrected in real-time by statistical filtering or spatial convolution of the kernel-associated image data accessible via a predetermined CSP kernels image-offsets structure.