假阳性悖论
计算机科学
再现性
模式识别(心理学)
人工智能
灵敏度(控制系统)
乳腺超声检查
卷积神经网络
乳腺癌
超声波
放射科
医学
癌症
统计
数学
乳腺摄影术
内科学
工程类
电子工程
作者
Amin Malekmohammadi,Sepideh Barekatrezaei,Ehsan Kozegar,Mohsen Soryani
出处
期刊:Ultrasonics
[Elsevier]
日期:2022-12-01
卷期号:: 106891-106891
标识
DOI:10.1016/j.ultras.2022.106891
摘要
Breast cancer mortality can be significantly reduced by early detection of its symptoms. The 3-D Automated Breast Ultrasound (ABUS) has been widely used for breast screening due to its high sensitivity and reproducibility. The large number of ABUS slices, and high variation in size and shape of the masses, make the manual evaluation a challenging and time-consuming process. To assist the radiologists, we propose a convolutional BiLSTM network to classify the slices based on the presence of a mass. Because of its patch-based architecture, this model produces the approximate location of masses as a heat map. The prepared dataset consists of 60 volumes belonging to 43 patients. The precision, recall, accuracy, F1-score, and AUC of the proposed model for slice classification were 84%, 84%, 93%, 84%, and 97%, respectively. Based on the FROC analysis, the proposed detector obtained a sensitivity of 82% with two false positives per volume.
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