假阳性悖论
计算机科学
假阳性和假阴性
灵敏度(控制系统)
人工智能
乳腺癌
编码(集合论)
体素
乳腺摄影术
乳腺超声检查
模式识别(心理学)
癌症
医学
工程类
程序设计语言
集合(抽象数据类型)
内科学
电子工程
作者
Yi Wang,Na Wang,Min Xu,Junxiong Yu,Chenchen Qin,Xiao Luo,Xin Yang,Tianfu Wang,Lixian Liu,Dong Ni
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2020-04-01
卷期号:39 (4): 866-876
被引量:111
标识
DOI:10.1109/tmi.2019.2936500
摘要
ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultrasound, ABUS attains operator-independent image acquisition and also provides 3D views of the whole breast. Nonetheless, reviewing ABUS images is particularly time-intensive and errors by oversight might occur. For this study, we offer an innovative 3D convolutional network, which is used for ABUS for automated cancer detection, in order to accelerate reviewing and meanwhile to obtain high detection sensitivity with low false positives (FPs). Specifically, we offer a densely deep supervision method in order to augment the detection sensitivity greatly by effectively using multi-layer features. Furthermore, we suggest a threshold loss in order to present voxel-level adaptive threshold for discerning cancer vs. non-cancer, which can attain high sensitivity with low false positives. The efficacy of our network is verified from a collected dataset of 219 patients with 614 ABUS volumes, including 745 cancer regions, and 144 healthy women with a total of 900 volumes, without abnormal findings. Extensive experiments demonstrate our method attains a sensitivity of 95% with 0.84 FP per volume. The proposed network provides an effective cancer detection scheme for breast examination using ABUS by sustaining high sensitivity with low false positives. The code is publicly available at https://github.com/nawang0226/abus_code.
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