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A CNN based method for automatic mass detection and classification in mammograms

机译:基于CNN的乳房X线图中的自动质量检测和分类方法

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A novel system for detection and classification of masses in breast mammography is introduced. The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score. While most of the previous work on mass identification in breast mammography has focused on classification, this study proposes to solve both the detection and the classification problems. The method is evaluated on a large multi-centre clinical data-set and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health care continues to accelerate generalising such an approach may have a profound impact on patient care in many applications.
机译:介绍了一种新的乳房乳房X线乳腺术检测和分类系统。该系统将乳房分割模块与基于修改的区域的卷积网络集成在一起,以根据BI-RADS分数获得质量的检测和分类。虽然以前的大多数乳房乳房X线摄影的群众识别工作都集中在分类上,但本研究提出了解决检测和分类问题。该方法在大型多中心临床数据集上进行评估,与专家放射科医师注释的地面真理相比。初步实验结果表明,通过建议的网络结构获得的高精度和效率。随着医疗保健数据的体积和复杂性继续加速推广,这种方法可能对许多应用中的患者护理产生深远的影响。

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