Journal of medical imaging (Bellingham, Wash.) · 2026
Illustration of transfer learning from breast cancer detection to risk prediction: adaptation to local data and local objectives.
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Wagner Tobias, Klanecek Zan, Wang Yao-Kuan, Cockmartin Lesley, Marshall Nicholas W, Studen Andrej, Jeraj Robert, Bosmans Hilde
Аннотация
PURPOSE: We aim to investigate whether a breast cancer risk model can be trained with transfer learning from a breast cancer detection model.
APPROACH: An existing open-source breast cancer detection model was used to extract the latent space representation of a local dataset composed of images from 17,878 breast cancer screening participants. An autoencoder network was used for dimensionality reduction. A final model training step mapped the reduced representation to 4-year breast cancer risk. Two experimental studies were performed: first, a local dataset was used to investigate the impact of transfer learning to local data. Second, the training was executed for breast cancer risk by excluding cases with obvious signs of cancer in the model training process, to avoid the influence of cancer signs. In addition, the Mirai model was evaluated.
RESULTS: The open-source model achieved a baseline performance of 0.72 with 95%CI [0.66, 0.78] for 4-year risk prediction when applied to local test cases of breast cancer risk data. This performance was improved after training on local breast cancer detection data to 0.75 with 95%CI [0.69, 0.81], although not statistically significant ( p = 0.09 ). Further training with local breast cancer risk data achieved an area under the curve (AUC) of 0.77 with 95%CI [0.71, 0.82], which is higher than the original model ( p < 0.01 ). The Mirai model demonstrated a lower performance after fine-tuning ( p < 0.01 ).
CONCLUSIONS: Transfer learning enabled the creation of a breast cancer risk prediction model from a previously developed breast cancer detection model, demonstrating that a publicly available model can be adapted to local clinical needs.
APPROACH: An existing open-source breast cancer detection model was used to extract the latent space representation of a local dataset composed of images from 17,878 breast cancer screening participants. An autoencoder network was used for dimensionality reduction. A final model training step mapped the reduced representation to 4-year breast cancer risk. Two experimental studies were performed: first, a local dataset was used to investigate the impact of transfer learning to local data. Second, the training was executed for breast cancer risk by excluding cases with obvious signs of cancer in the model training process, to avoid the influence of cancer signs. In addition, the Mirai model was evaluated.
RESULTS: The open-source model achieved a baseline performance of 0.72 with 95%CI [0.66, 0.78] for 4-year risk prediction when applied to local test cases of breast cancer risk data. This performance was improved after training on local breast cancer detection data to 0.75 with 95%CI [0.69, 0.81], although not statistically significant ( p = 0.09 ). Further training with local breast cancer risk data achieved an area under the curve (AUC) of 0.77 with 95%CI [0.71, 0.82], which is higher than the original model ( p < 0.01 ). The Mirai model demonstrated a lower performance after fine-tuning ( p < 0.01 ).
CONCLUSIONS: Transfer learning enabled the creation of a breast cancer risk prediction model from a previously developed breast cancer detection model, demonstrating that a publicly available model can be adapted to local clinical needs.