Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
复制标题
使用放射组学作为胸部 X 光检查中胸部疾病分类和定位的先验知识
DOI：
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发表时间：
2020
期刊：
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通讯作者：
Yifan Peng
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作者：
Yan Han;Chongyan Chen;Liyan Tang;Mingquan Lin;Ajay Jaiswal;Song Wang;A. Tewfik;G. Shih;Ying Ding;Yifan Peng
胸部X射线由于其非侵入性而成为最常见的医学诊断之一。胸部X光图像的数量激增，但阅读胸部X光片仍然是由放射科医生手动执行的，这造成了巨大的消耗和延迟。传统上，放射组学作为放射学的一个子领域，可以从医学图像中提取大量的定量特征，在深度学习时代之前，它展示了其促进医学成像诊断的潜力。在本文中，我们开发了一个端到端的框架，ChexRadiNet，可以利用放射组学的功能，以提高异常分类性能。具体来说，ChexRadiNet首先应用一种轻量级但有效的三重注意机制来对胸部X射线进行分类并突出显示异常区域。然后使用生成的类激活图提取放射组学特征，这进一步指导我们的模型学习更鲁棒的图像特征。经过多次迭代，并在放射组学特征的帮助下，我们的框架可以收敛到更准确的图像区域。我们使用三个公共数据集评估ChexRadiNet框架：NIH ChestX-ray，CheXpert和MIMIC-CXR。我们发现ChexRadiNet在疾病检测（AUC为0.843）和定位（T（IoU）= 0.1为0.679）方面都优于最先进的技术。我们在https：//github上公开了代码。com/bionlplab/lung_disease_detection_amia2021，希望这种方法可以促进对放射学世界有更高层次理解的自动系统的开发。
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. In this paper, we develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. Specifically, ChexRadiNet first applies a light-weight but efficient triplet-attention mechanism to classify the chest X-rays and highlight the abnormal regions. Then it uses the generated class activation map to extract radiomic features, which further guides our model to learn more robust image features. After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR. We find that ChexRadiNet outperforms the state-of-the-art on both disease detection (0.843 in AUC) and localization (0.679 in T(IoU) = 0.1). We make the code publicly available at https://github. com/bionlplab/lung_disease_detection_amia2021, with the hope that this method can facilitate the development of automatic systems with a higher-level understanding of the radiological world.