Bounded-abstaining classification for breast tumors in imbalanced ultrasound images - Guan, Hongjiao; Zhang, Yingtao; Cheng, Heng-Da; Tang, Xianglong (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: breast ultrasound (BUS) images; reliable diagnosis; abstaining classification; imbalanced datasets