Transfer Learning from Breast Cancer Detection Models for Image-Based Breast Cancer Risk Prediction

Aim: This study proposes a method to bypass the requirement of large amounts of original training data to develop a 1- to 4-year breast cancer risk prediction model using transfer learning from a breast cancer detection model with digital mammography images as input. Methods: The study utilizes a labelled dataset of 423 low risk cases and 423 high risk cases, which is considered a small amount of data in terms of AI development, but from the viewpoint of a regional screening organization this represents a large number of high risk cases, given the rarity of such cases compared to the large number of low risk cases available. A breast cancer detection model was used to obtain a latent representation of features extracted from 'FOR PRESENTATION' screening mammography images from three systems from a single vendor (Siemens). Dimensionality reduction was performed on the latent space using an Autoencoder architecture. The reduced latent space was then mapped to 1- to 4-year breast cancer risk with a fully-connected model. Results: The resulting model achieved an AUC of 0.77 for differentiating high and low risk cases, outperforming the Tyrer-Cuzick model and achieving state-of-the-art performance. Conclusions: The use of transfer learning from breast cancer detection models can produce image-based breast cancer risk prediction models that are comparable to the state-of-the-art, while requiring only moderate amounts of data.