Multi-task learning to improve performance consistency in mammogram classification
Mickael Tardy, Diana Mateus
Breast cancer is the most prevalent cancer amongst women. Its regular screening, often based on mammograms, significantly reduces the mortality. Deep learning has shown good performances in coping with screening-generated imaging data, however there are still open questions related to the imbalance, noisiness, and heterogeneity of the data. We propose to address these challenges with Multi-Task Learning, combining tasks such as classification, regression, segmentation, and reconstruction. Our approach allows to obtain consistent performances of AUC $\approx 0.80$ across different vendors (including those unknown during training) on the primary breast cancer classification task, while fulfilling well secondary tasks including an $F_1$ score of $0.96$ on 4-class vendor classification, and $F_1$ score of 0.64 on 4-class density classification.
Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)