Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

Applied Sciences

Alessandra Retico¹  Camilla Scapicchio¹  Maria Evelina Fantacci¹  Francesco Laruina¹  Francesca Lizzi² 

[1] National Institute for Nuclear Physics, Pisa Division, I-56127 Pisa, Italy;Scuola Normale Superiore, I-56126 Pisa, Italy;

关键词: explainability;    deep learning;    mammography;    breast density;   

DOI  :  10.3390/app12010148

来源: DOAJ

【 摘 要 】

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman’s rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems.

【 授权许可】

Unknown