DIB-MG learns radiologic features from large scale images without any human annotations.
For the algorithm development, a total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included in the data sets.
The core algorithm of DIB-MG is a deep convolutional neural network – a deep learning algorithm specialised for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth.