Transfer learning for breast cancer malignancy classification based on dynamic contrast-enhanced MR images - Haarburger, Christoph; Langenberg, Peter; Truhn, Daniel; Schneider, Hannah; Thüring, Johannes; Schrading, Simone; Kuhl, Christiane K.; Merhof, Dorit (Informatik aktuell, 2018)
In clinical contexts with very limited annotated data, such as breast cancer diagnosis, training state-of-the art deep neural networks is not feasible. As a solution, we transfer parameters of networks pretrained on natural RGB images to malignancy classification of breast lesions in dynamic contrast-enhanced MR images. Since DCE-MR images comprise several contrasts and timepoints, a direct finetuning of pretrained networks expecting three input channels is not possible. Based on the hypothesis that a subset of the acquired image data is sufficient for a computer-aided diagnosis, we provide an experimental comparison of all possible subsets of MR image contrasts and determine the best combination for malignancy classification. A subset of images acquired at three timepoints of dynamic T1-weighted images which closely corresponds to human interpretation performs best with an AUC of 0.839.
CRIS-Team Scopus Importer:2023-03-23