Using deep learning for triple-negative breast cancer classification in DCE-MRI

Joel Vidal; Robert Martí

doi:10.1117/12.2625780

13 July 2022 Using deep learning for triple-negative breast cancer classification in DCE-MRI

Joel Vidal, Robert Martí

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860X (2022) https://doi.org/10.1117/12.2625780
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Triple-negative is one of the most aggressive type of breast cancer for which is also difficult to find an effective treatment. An early diagnosis and a fast and specific treatment are shown to be key aspects for a better prognosis. Current diagnosis of these cases are based on performing a biopsy. This study proposes a non-invasive medical imaging predication method, based on a deep learning architecture, to automatically classify triple-negative tumors in DCE-MRI images. Results are evaluated on an extensive public dataset for different normalizations, data augmentations, learning rates and batch sizes, reaching a state-of-the-art AUC of 0.68.

Citation Download Citation

Joel Vidal and Robert Martí "Using deep learning for triple-negative breast cancer classification in DCE-MRI", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860X (13 July 2022); https://doi.org/10.1117/12.2625780

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