Researchers led by Prof. Susan M. Astley from the University of Manchester, United Kingdom, recently developed and tested a new deep learning-based model capable of estimating breast density with high precision. Their findings are published in the Journal of Medical Imaging.
Instead of building the model from the ground up, they used two independent deep learning models that were initially trained on ImageNet, a non-medical imaging dataset with over a million images. This approach, known as “transfer learning,” allowed them to train the models more efficiently with fewer medical imaging data.
Using nearly 160,000 full-field digital mammogram images that were assigned density values on a visual analogue scale by experts (radiologists, advanced practitioner radiographers, and breast physicians) from 39,357 women, the researchers developed a procedure for estimating the density score for each mammogram image. The objective was to take in a mammogram image as input and churn out a density score as output.
The procedure involved preprocessing the images to make the training process computationally less intensive, extracting features from the processed images with the deep learning models, mapping the features to a set of density scores, and then combining the scores using an ensemble approach to produce a final density estimate.
With this approach, the researchers developed highly accurate models for estimating breast density and its correlation with cancer risk, while conserving the computation time and memory.
