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首页> 外文会议>Conference on Medical Imaging: Computer-Aided Diagnosis >Estimating near term breast cancer risk from sequential mammograms using deep learning, Radon Cumulative Distribution Transform, and a clinical risk factor: preliminary analysis
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Estimating near term breast cancer risk from sequential mammograms using deep learning, Radon Cumulative Distribution Transform, and a clinical risk factor: preliminary analysis

机译：利用深度学习，氡累积分布变换和临床风险因素估算顺序乳腺X XMIMPORMS附近的乳腺癌风险：初步分析

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Our ultimate goal is to develop a personalized breast screening model. This model will provide information to an individual woman for planning her own screening options based on her imaging and other characteristics. To do so, we are developing a computerized method to estimate a near term risk of screen detectable breast cancer (sBCa) by analyzing lateral and temporal differences in screening mammograms using the Radon Cumulative Distribution Transform (RCDT), a deep convolutional neural network (dCNN). The RCDT can highlight subtle signals associated with future sBCa by exploiting lateral and temporal differences in screening mammograms of women. We used a dCNN to build a robust classification marker of having sBCa within a year from a negative screening. We explored the potential for incremental improvement over known risk factors by combining the dCNN with age and showed consistent gains in identifying asymptomatic women with a low risk of having sBCa (who could use the personalized screening option to safely skip their next screening). We used 4296 screening mammogram images of 537 women with 151 sBCa cases and fine-tuned the VGG16 network using a data split ratio of 0.64:0.16:0.2 for training, validation, and testing. The final model on the independent testing dataset achieved an AUC of 0.82. and a specificity at a sensitivity level of 98% (Sp@Se98) of 0.51 with a 95% confidence interval of 0.40 to 0.65. This indicates that the model could reliably identify at least 40% of low-risk women with a 2% error for a false negative rate.
机译：我们的最终目标是开发一个个性化的乳房筛选模型。该模型将提供给个人女性的信息，以根据她的成像和其他特征规划自己的筛选选项。为此，我们正在开发一种计算机化方法来估计筛选筛选乳腺癌（SBCA）的近期风险，通过分析使用氡累积分布变换（RCDT），深卷积神经网络（DCNN ）。 RCDT可以通过利用女性筛选乳房X线图的横向和时间差异来突出与未来SBCA相关的微妙信号。我们使用DCNN在否定筛选中建立一个具有SBCA的强大分类标记。我们探讨了通过将DCNN与年龄组合的DCNN与已知风险因素的增量改善的潜力，并在识别具有SBCA的风险低的无症状妇女方面表现出一致的增益（谁可以使用个性化筛选选项来安全地跳过他们的下一个筛查）。我们使用了4296个筛选乳房X线照片537名女性的乳房X线照片，使用151个SBCA病例和微调VGG16网络，使用0.64：0.16：0.2进行培训，验证和测试。独立测试数据集的最终模型实现了0.82的AUC。并且在98％（SP @ SE98）的灵敏度水平的特异性为0.51，95％置信区间为0.40至0.65。这表明该模型可以可靠地识别至少40％的低风险女性，误差为2％误差。

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