Abstract: The present invention relates to the provision of an automated system and a computer-implemented method for detecting a disease such as nasopharyngeal cancer (NPC) based upon the use of either a Fuzzy Inference (FI) system or a deep learning-fuzzy inference (DeLFI) hybrid model to analyse immunofluorescence assay (IFA) images. For NPC detection, the system and method of the invention would distinguish between Epstein Barr Virus (EBV) Early Antigen (EA) positive and negative cells, and identify cellular patterns which are indicative of NPC. The DeLFI hybrid model requires less human evaluation and thereby has the potential to improve the scalability and accuracy of NPC detection.

Abstract: The present invention relates to the field of diagnostics and more particularly to the detection of beta-hemolytic pathogens. More specifically, the present invention relates to the rapid and accurate detection of beta-hemolytic pathogens using sterically-stabilized liposomes.

Abstract: The present invention relates to the field of diagnostics and more particularly to the detection of beta-hemolytic pathogens. More specifically, the present invention relates to the rapid and accurate detection of beta-hemolytic pathogens using sterically-stabilized liposomes.

Abstract: The present invention relates to the field of diagnostics and more particularly to the detection of beta-hemolytic pathogens. More specifically, the present invention relates to the rapid and accurate detection of beta-hemolytic pathogens using sterically-stabilized liposomes.

Abstract: An improved system and method for digital image classification is provided. A host computer having a processor is coupled to a memory storing thereon reference feature data. A graphics processing unit (GPU) having a processor is coupled to the host computer and is configured to obtain, from the host computer, feature data corresponding to the digital image; to access, from the memory, the one or more reference feature data; and to determine a semi-metric distance based on a Poisson-Binomial distribution between the feature data and the one or more reference feature data. The host computer is configured to classify the digital image using the determined semi-metric distance.

Abstract: The present invention relates to the field of diagnostics and more particularly to the detection of beta-hemolytic pathogens. More specifically, the present invention relates to the rapid and accurate detection of beta-hemolytic pathogens using sterically-stabilized liposomes.

Abstract: An improved system and method for digital image classification is provided. A host computer having a processor is coupled to a memory storing thereon reference feature data. A graphics processing unit (GPU) having a processor is coupled to the host computer and is configured to obtain, from the host computer, feature data corresponding to the digital image; to access, from the memory, the one or more reference feature data; and to determine a semi-metric distance based on a Poisson-Binomial distribution between the feature data and the one or more reference feature data. The host computer is configured to classify the digital image using the determined semimetric distance.