乳腺摄影术
计算机科学
人工智能
数字乳腺摄影术
癌症检测
单发
计算机视觉
接收机工作特性
乳腺癌
模式识别(心理学)
癌症
医学
机器学习
光学
物理
内科学
作者
Yinhao Ren,Rui Hou,Dehan Kong,Yue Geng,Lars J. Grimm,Jeffrey R. Marks,Joseph Y. Lo
出处
期刊:Medical Imaging 2019: Computer-Aided Diagnosis
日期:2019-03-13
被引量:1
摘要
Detection of suspicious breast cancer lesion in screening mammography images is an important step for the downstream diagnosis the of breast cancer. A trained radiologist can usually take advantage of multi-view correlation of suspicious lesions to locate abnormalities. In this work, we investigate the feasibility of using a random image pair of the same breast from the same exam for the detection of suspicious lesions. We present a novel approach to utilize a single shot detection system inspired by You only look once (YOLO) v1 to simultaneously process a primary detection view and a secondary view for the localization of lesion in the primary detection view. We used a combination of screening exams from Duke University Hospital and OPTIMAM to conduct our experiments. The Duke dataset includes 850 positive cases and around 10,000 negative cases. The OPTIMAM dataset includes around 350 cases. We observed a consistent left shift of the Free-Response Receiver Operating Characteristic (FROC) curve in the multi-view detection model compared to the single-view detection model. This result is promising for future development of automated lesion detection systems focusing on modern full-field digital mammography (FFDM).
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