This involved preprocessing images to make the task less computationally intensive, the experts explained. The deep learning models would then extract features from the processed images and map them out with a set of density scores. The models’ scores were then combined to give a final estimate of breast density.
Not only did the final product offer accurate breast density assessments, but it also required less computation time/resources and memory. This approach could make future model development a less time intensive process that requires significantly less data, the team explained.
“... we have demonstrated that using a transfer learning approach with deep features results in accurate breast density predictions,” the authors wrote. “This approach is computationally fast and cheap, which can enable more analysis to be done and smaller datasets to be used.”
Of note, the two pretrained models used in this work were trained on a nonmedical data set. Model performance would likely show improvement with the inclusion of medical images, the team suggested.
The study is available in the Journal of Medical Imaging.
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April 11, 2023 at 02:00AM
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