Artificial intelligence for image-based breast cancer risk prediction using attention

Stepan Romanov; Sacha Howell; Elaine Harkness; Megan Bydder; D. Gareth Evans; Steven Squires; Martin Fergie; Sue Astley

doi:10.1117/12.2653875

7 April 2023 Artificial intelligence for image-based breast cancer risk prediction using attention

Stepan Romanov, Sacha Howell, Elaine Harkness, Megan Bydder, D. Gareth Evans, Steven Squires, Martin Fergie, Sue Astley

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246526 (2023) https://doi.org/10.1117/12.2653875
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. 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. Machine learning enabled automatic risk prediction provides key advantages over existing methods such as the ability to extract more complex information from mammograms. 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. Additionally, this increases the interpretability of our model as important patches can be shown in a saliency map. 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 0.635 (0.600,0.669) in cancer-free screening mammograms of women who went on to a screen-detected or interval cancer between 5 and 55 months and an AUC of 0.804 (0.777,0.830) in screen-detected cancers.

Citation Download Citation

Stepan Romanov, Sacha Howell, Elaine Harkness, Megan Bydder, D. Gareth Evans, Steven Squires, Martin Fergie, and Sue Astley "Artificial intelligence for image-based breast cancer risk prediction using attention", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246526 (7 April 2023); https://doi.org/10.1117/12.2653875

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