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Paper details
Number 2 - June 2020
Volume 30 - 2020
Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
Hongjiao Guan, Yingtao Zhang, Heng-Da Cheng, Xianglong Tang
Abstract
Computer-aided breast ultrasound (BUS) diagnosis remains a difficult task. One of the challenges is that imbalanced BUS
datasets lead to poor performance, especially with regard to low accuracy in the minority (malignant tumor) class. Missed
diagnosis of malignant tumors can cause serious consequences, such as delaying treatment and increasing the risk of death.
Moreover, many diagnosis methods do not consider classification reliability; thus, some classifications may have a large
uncertainty. To resolve such problems, a bounded-abstaining classification model is proposed. It maximizes the area under
the ROC curve (AUC) under two abstention constraints. A total of 219 (92 malignant and 127 benign) BUS images are
collected from the First Affiliated Hospital of Harbin Medical University, China. The experiment tests BUS datasets of
three imbalance levels, and the performance contours are analyzed. The results demonstrate that AUC-rejection curves are
less affected by class imbalance than accuracy-rejection curves. Compared with the state-of-the-art, the proposed method
yields a significantly larger AUC and G-mean using imbalanced BUS datasets.
Keywords
breast ultrasound (BUS) images, reliable diagnosis, abstaining classification, imbalanced datasets