RadTex：从文本报告中学习高效的X光片表示
RadTex: Learning Efficient Radiograph Representations from Text Reports
AI总结：
本文提出RadTex框架，利用放射学报告进行图像描述预训练，在有限标注数据下提升胸部X光片分类性能，较ImageNet和域内监督预训练表现更优。
摘要：
利用深度学习对胸部X光片进行自动化分析，在增强患者疾病临床诊断方面具有巨大潜力。然而，深度学习模型通常需要大量标注数据才能实现高性能——这往往是医疗领域适应的障碍。本文构建了一个数据高效的学习框架，利用放射学报告在有限标注数据（少于1000个样本）下提升医学图像分类性能。具体而言，我们研究通过图像描述预训练学习高质量医学图像表示，使其在更少样本上完成训练。在对卷积编码器和Transformer解码器进行联合预训练后，我们将学习到的编码器迁移至多种分类任务。在9种病理上的平均结果表明，当标注训练数据有限时，本模型比ImageNet监督预训练和域内监督预训练取得更高分类性能。
英文摘要：
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typically require large amounts of annotated data to achieve high performance -- often an obstacle to medical domain adaptation. In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples). Specifically, we examine image-captioning pretraining to learn high-quality medical image representations that train on fewer examples. Following joint pretraining of a convolutional encoder and transformer decoder, we transfer the learned encoder to various classification tasks. Averaged over 9 pathologies, we find that our model achieves higher classification performance than ImageNet-supervised and in-domain supervised pretraining when labeled training data is limited.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
