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Abstract #2390

Augmented ensemble learning is effective strategy for imbalanced small dataset: improve differentiation of low from high grade prostate cancer

Yuta Akamine1, Yoshiko Ueno2, Keitaro Sofue2, Takamichi Murakami2, Yu Ueda1, Ahsan Budrul1, Masami Yoneyama1, Makoto Obara1, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Department of Radiology, Kobe University Graduate School of Medicine, Hyogo, Japan, 3Asia Pacific, Philips Healthcare, Tokyo, Japan

Machine learning (ML) techniques have gained more attention to distinguish low from high grade prostate cancer. However, obtaining big training data is difficult. Moreover, ML models created by imbalanced dataset have a high accuracy for majority, but a low accuracy for minority. For this problem, data augmentation is widely studied. Recently, ensemble learning, which merges different classifiers, has shown great potential. Combinations of data augmentation and ensemble learning were investigated, using multi-parametric MR. We demonstrated that synthetic-minority-over-sampling-technique (SMOTE) with ensemble learning showed increased F1 (0.831) and AUC (0.762) and is effective strategy to improve diagnosis performance for imbalanced small dataset.

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