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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线照片质量自动检测和分类方法

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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:介绍了一种用于乳腺钼靶中肿块检测和分类的新型系统。该系统将乳房分割模块与改进的基于区域的卷积网络集成在一起,以根据BI-RADS分数获得对肿块的检测和分类。尽管先前有关乳腺X线摄影中质量识别的大多数工作都集中在分类上,但本研究建议解决检测和分类问题。该方法在大型的多中心临床数据集上进行评估,并与专家放射线师注释的地面真相进行比较。初步实验结果表明,所建议的网络结构具有较高的准确性和效率。随着医疗保健中数据量和复杂性的不断加快,这种方法可能会在许多应用中对患者护理产生深远的影响。

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