Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for indicating whether additional medical tests are required after analysing an initial medical screening, in substantially real-time. Aspects and/or embodiments seek to provide a method and system for recommending additional medical tests, in substantially real-time, based on analysing an initial medical scan, with the use of deep learning.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: The present invention relates to a method and system that automatically determines malignancy in mammograms in parallel with a human operator. More particularly, the present invention relates to providing a reliable automated malignancy determination in parallel to a human operator to reduce the need for two human operators in a mammography analysis workflow. Aspects and/or embodiments seek to provide a method of automatically assessing mammography data in parallel with a human operator. Aspects and/or embodiments also seek to address the problems relating to providing a substantially reliable second reader to allow a single operator to analyse and diagnose mammography data.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for indicating whether additional medical tests are required after analysing an initial medical screening, in substantially real-time. Aspects and/or embodiments seek to provide a method and system for recommending additional medical tests, in substantially real-time, based on analysing an initial medical scan, with the use of deep learning.

Abstract: The present invention relates to a method and system that automatically determines malignancy in mammograms in parallel with a human operator. More particularly, the present invention relates to providing a reliable automated malignancy determination in parallel to a human operator to reduce the need for two human operators in a mammography analysis workflow. Aspects and/or embodiments seek to provide a method of automatically assessing mammography data in parallel with a human operator. Aspects and/or embodiments also seek to address the problems relating to providing a substantially reliable second reader to allow a single operator to analyse and diagnose mammography data.