Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification

Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.

doi:10.1002/mp.13054

Cited by 1 publication

(1 citation statement)

References 20 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Morphology and texture features are two main categories that describe and analyze breast tumor images (Lee et al, 2018). According to the BI-RADS, 13 morphological features describing the shape, margin, and orientation of tumors were extracted from each BUS image (Cheng et al, 2010;Moon et al, 2018).…”

Section: 2mentioning

confidence: 99%

Bounded-abstaining classification for breast tumors in imbalanced ultrasound images

Guan

Zhang

Cheng

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: 2mentioning

confidence: 99%

Bounded-abstaining classification for breast tumors in imbalanced ultrasound images

Guan

Zhang

Cheng

et al. 2020

View full text Add to dashboard Cite

show abstract

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification

Abstract: Computer segmentation is an indirect variable affecting the computer classification. As better segmentation does not guarantee better classification, we should report both segmentation and classification performances when comparing segmentation algorithms.

Cited by 1 publication

References 20 publications

Bounded-abstaining classification for breast tumors in imbalanced ultrasound images

Bounded-abstaining classification for breast tumors in imbalanced ultrasound images

Contact Info

Product

Resources

About