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The prevention and early detection of breast cancer hinges on precise prediction of individual breast cancer risk. Whilst well-established clinical risk factors can be used to stratify the population into risk groups, the addition of genetic information and breast density has been shown to improve prediction. Deep learning based approach have been shown to automatically extract complex information from images. However, this is a challenging area of research, partly due to the lack of data within the field, therefore there is scope for novel approaches. Our method uses Multiple Instance Learning in tandem with attention in order to make accurate, short-term risk predictions from full-sized mammograms taken prior to the detection of cancer. This approach ensures small features like calcifications are not lost in a downsizing process and the whole mammogram is analysed effectively. An attention pooling mechanism is designed to highlight patches of increased importance and improve performance. We also use transfer learning in order to utilise a rich source of screen-detected cancers and evaluate whether a model trained to detect cancers in mammograms allows us also to predict risk in priors. Our model achieves an AUC of 0.620 (0.585,0.657) in cancer-free screening mammograms of women who went on to a screen-detected or interval cancer between 5 and 55 months later, including for common breast cancer risk factors. Additionally, our model is able to discriminate interval cancers at an AUC of 0.638 (0.572, 0.703) and highlights the potential for such a model to be used alongside national screening programmes.
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