PDF(Pubmed)
Abstract：
OBJECTIVE: Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.
METHODS: We collected 953 breast ultrasound images from two open-source datasets and classified them with help of an expert radiologist according to BI-RADS criteria. The data was split into normal, benign and malignant classes. We then used machine learning to develop classification and segmentation algorithms.
RESULTS: We found 3.92% of the images across the open-source datasets had erroneous classifications. Post-radiologist intervention, three algorithms were developed based on the classification categories. Classification algorithms distinguished images with healthy breast tissue from those with abnormal tissue with 96% accuracy, and distinguished benign from malignant images with 85% accuracy. Both algorithms generated robust F1 and AUROC metrics. Finally, the masses within images were segmented with an 80.31% DICE score.
CONCLUSIONS: Our work illustrates the potential of deep learning algorithms to improve the accuracy of breast ultrasound assessments and to facilitate automated assessments.
METHODS: We collected 953 breast ultrasound images from two open-source datasets and classified them with help of an expert radiologist according to BI-RADS criteria. The data was split into normal, benign and malignant classes. We then used machine learning to develop classification and segmentation algorithms.
RESULTS: We found 3.92% of the images across the open-source datasets had erroneous classifications. Post-radiologist intervention, three algorithms were developed based on the classification categories. Classification algorithms distinguished images with healthy breast tissue from those with abnormal tissue with 96% accuracy, and distinguished benign from malignant images with 85% accuracy. Both algorithms generated robust F1 and AUROC metrics. Finally, the masses within images were segmented with an 80.31% DICE score.
CONCLUSIONS: Our work illustrates the potential of deep learning algorithms to improve the accuracy of breast ultrasound assessments and to facilitate automated assessments.
摘要：
目的：对乳腺超声图像中的肿瘤进行自动分类和分割，从而为乳腺癌患者提供更好的诊断和计划治疗策略。
方法：我们从两个开源数据集中收集了953张乳腺超声图像，并在放射科专家的帮助下根据BI-RADS标准对其进行分类。数据被分成正常的，良性和恶性类别。然后，我们使用机器学习来开发分类和分割算法。
结果：我们发现，在开源数据集中，3.92%的图像有错误的分类。放射科医师介入后,根据分类类别开发了三种算法。分类算法以96%的准确率区分健康乳腺组织和异常组织的图像，并以85%的准确率区分良性和恶性图像。两种算法都生成了稳健的F1和AUROC度量。最后,图像内的肿块以80.31%DICE评分进行分割.
结论：我们的工作说明了深度学习算法在提高乳腺超声评估的准确性和促进自动评估方面的潜力。
方法：我们从两个开源数据集中收集了953张乳腺超声图像，并在放射科专家的帮助下根据BI-RADS标准对其进行分类。数据被分成正常的，良性和恶性类别。然后，我们使用机器学习来开发分类和分割算法。
结果：我们发现，在开源数据集中，3.92%的图像有错误的分类。放射科医师介入后,根据分类类别开发了三种算法。分类算法以96%的准确率区分健康乳腺组织和异常组织的图像，并以85%的准确率区分良性和恶性图像。两种算法都生成了稳健的F1和AUROC度量。最后,图像内的肿块以80.31%DICE评分进行分割.
结论：我们的工作说明了深度学习算法在提高乳腺超声评估的准确性和促进自动评估方面的潜力。
