用低分辨率胸片和带噪标签训练模型，提升远程医疗诊断准确率
Chest X-ray Classification using Deep Convolution Models on Low-resolution images with Uncertain Labels
- 设计随机标签翻转策略应对标注噪声
- 在5种肺部疾病上实现3%的准确率提升
- 适合资源受限场景下的医学影像自动诊断
深度卷积神经网络在高质量医学影像任务中表现优异，但实际应用中常面临低分辨率图像问题。本文针对胸片分类任务，研究不同输入尺寸对深度CNN模型的影响，并利用随机标签翻转技术处理数据集中的噪声标签。采用多标签分类模型的集成方法，基于公开的CheXpert数据集中的14种病理中的5种进行训练。通过数据增强、正则化等手段提升性能，并使用类激活图可视化模型决策过程。与原始论文中200名受试者高分辨率图像的分类结果对比，对心影增大、实变和肺水肿三种病灶，本模型准确率提升3%。
原文摘要 · Abstract (English)
Deep Convolutional Neural Networks have consistently proven to achieve state-of-the-art results on a lot of imaging tasks over the past years' majority of which comprise of high-quality data. However, it is important to work on low-resolution images since it could be a cheaper alternative for remote healthcare access where the primary need of automated pathology identification models occurs. Medical diagnosis using low-resolution images is challenging since critical details may not be easily identifiable. In this paper, we report classification results by experimenting on different input image sizes of Chest X-rays to deep CNN models and discuss the feasibility of classification on varying image sizes. We also leverage the noisy labels in the dataset by proposing a Randomized Flipping of labels techniques. We use an ensemble of multi-label classification models on frontal and lateral studies. Our models are trained on 5 out of the 14 chest pathologies of the publicly available CheXpert dataset. We incorporate techniques such as augmentation, regularization for model improvement and use class activation maps to visualize the neural network's decision making. Comparison with classification results on data from 200 subjects, obtained on the corresponding high-resolution images, reported in the original CheXpert paper, has been presented. For pathologies Cardiomegaly, Consolidation and Edema, we obtain 3% higher accuracy with our model architecture.
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