论文标题
乳房X线图图像分类的无监督域的适应性：模型概括的有前途的工具
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
论文作者
论文摘要
概括是深度学习模型在医学图像中的临床验证和应用中的关键挑战之一。研究表明，由于患者群体和图像设备配置的差异，因此在公开数据集中接受培训的模型通常无法在现实世界中效果很好。同样，手动注释临床图像很昂贵。在这项工作中，我们提出了使用自行车gan的无监督域适应（UDA）方法，以提高模型的概括能力，而无需使用任何其他手动注释。
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.
