Zusammenfassung
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.
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Haarburger, C. et al. (2018). Transfer Learning for Breast Cancer Malignancy Classification based on Dynamic Contrast-Enhanced MR Images. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_61
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DOI: https://doi.org/10.1007/978-3-662-56537-7_61
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-56537-7
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