论文标题
使用远距离学习和区域验证的胸病鉴定和定位
Thoracic Disease Identification and Localization using Distance Learning and Region Verification
论文作者
论文摘要
最近，使用深度学习模型在医学图像中识别和定位疾病最近引起了重大兴趣。现有方法仅考虑独立使用每个图像训练网络，并最大程度地利用激活图来定位。在本文中，我们提出了一种替代方法，该方法在图像的三胞胎和周期性训练区域特征中学习判别特征，以验证细心区域是否包含疾病的信息。具体而言，我们适应了一个多标签疾病分类的远程学习框架，以区分微妙的疾病特征。此外，我们在培训过程中将预测的特定区域的特定区域的特征馈送到单独的分类器中，以更好地验证局部疾病。我们的模型可以在具有挑战性的ChestX-Ray14数据集上实现最新的分类性能，而我们的消融研究表明，远程学习和区域验证都有助于整体分类性能。此外，距离学习和区域验证模块可以捕获比没有这些模块的基线模型更好的本地化的基本信息。
The identification and localization of diseases in medical images using deep learning models have recently attracted significant interest. Existing methods only consider training the networks with each image independently and most leverage an activation map for disease localization. In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Concretely, we adapt a distance learning framework for multi-label disease classification to differentiate subtle disease features. Additionally, we feed back the features of the predicted class-specific regions to a separate classifier during training to better verify the localized diseases. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance. Moreover, the distance learning and region verification modules can capture essential information for better localization than baseline models without these modules.
