A deep learning framework to classify breast density with noisy labels regularization. (June 2022) - Lopez-Almazan, Hector; Javier Pérez-Benito, Francisco; Larroza, Andrés; Perez-Cortes, Juan-Carlos; Pollan, Marina; Perez-Gomez, Beatriz; Salas Trejo, Dolores; Casals, María; Llobet, Rafael
Highlights: A preprocess that allows a correct breast segmentation in mammographies with noisy background. A preprocess that adjust the intensities to eliminate problems such as unusual brightness. An intuitive preprocess protocol that normalizes the gray level variability caused by different acquisition devices or different capture processes. The implementation of a convolution-based architecture capable of modeling multiple radiologists' opinions, thus reducing the existing variability caused by the noisy labels. The results suggest this system behaves like a radiologist in the task of classifying mammograms according to their breast density. Abstract: Background and Objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.