Softmax函数
人工智能
深度学习
分割
计算机科学
模式识别(心理学)
像素
乳房成像
乳房磁振造影
磁共振成像
医学
人工神经网络
放射科
机器学习
乳腺癌
乳腺摄影术
癌症
内科学
作者
Michael Z. Liu,Cara Swintelski,Shen-Su Sun,Maham Siddique,Elise Desperito,Sachin Jambawalikar,Richard Ha
标识
DOI:10.1016/j.acra.2021.03.032
摘要
To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification.In this IRB approved study, the dataset consisted of 278,685 image slices from 438 patients. The weakly supervised network was based on the Resnet-101 architecture. Training was implemented using the Adam optimizer and a final SoftMax score threshold of 0.5 was used for two class classification (malignant or benign). 278,685 image slices were combined into 92,895 3-channel images. 79,871 (85%) images were used for training and validation while 13,024 (15%) images were separated for testing. Of the testing dataset, 11,498 (88%) were benign and 1531 (12%) were malignant. Model performance was assessed.The weakly supervised network achieved an AUC of 0.92 (SD ± 0.03) in distinguishing malignant from benign images. The model had an accuracy of 94.2% (SD ± 3.4) with a sensitivity and specificity of 74.4% (SD ± 8.5) and 95.3% (SD ± 3.3) respectively.It is feasible to use a weakly supervised deep learning approach to assess breast MRI images without the need for pixel-by-pixel segmentation yielding a high degree of specificity in lesion classification.
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