Transfer learning from breast cancer detection models for image-based breast cancer risk prediction

T. Wagner; Z. Klanecek; Y. K. Wang; L. Cockmartin; N. Marshall; A. Studen; R. Jeraj; H. Bosmans

doi:10.1117/12.3006670

3 April 2024 Transfer learning from breast cancer detection models for image-based breast cancer risk prediction

T. Wagner, Z. Klanecek, Y. K. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292725 (2024) https://doi.org/10.1117/12.3006670
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

T. Wagner, Z. Klanecek, Y. K. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, and H. Bosmans "Transfer learning from breast cancer detection models for image-based breast cancer risk prediction", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292725 (3 April 2024); https://doi.org/10.1117/12.3006670

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