用人工智能生成医生看片路径，提升肺部X光病灶识别准确率
Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
- 用循环神经网络生成类人看片路径
- 在20万张胸片上实现14种病灶的多标签分类性能提升
- 适合医疗影像智能诊断研究者参考
放射科专家通过依次注视解剖结构来诊断胸部X光片。自动多标签疾病分类器可借鉴医生的视觉扫描策略。然而，大多数胸片缺乏真实扫描路径数据，限制了深度学习模型的表现。本文提出利用视觉扫描路径预测模型生成有效的人工扫描路径，并构建结合路径与图像特征的多标签分类框架。扫描路径预测采用循环神经网络，分类器则采用含注意力机制的迭代序列模型。实验表明，生成路径具有类人特征，且使用人工路径显著提升了多标签分类性能。基于两个常用数据集中的约20万张胸片，对14种病理发现进行评估。
原文摘要 · Abstract (English)
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorporating aspects of the radiologists' approach. Recorded visual scanpaths of radiologists on CXR images can be used for the said purpose. But, such scanpaths are not available for most CXR images, which creates a gap even for modern deep learning based classifiers. This paper proposes to mitigate this gap by generating effective artificial visual scanpaths using a visual scanpath prediction model for CXR images. Further, a multi-class multi-label classifier framework is proposed that uses a generated scanpath and visual image features to classify diseases in CXR images. While the scanpath predictor is based on a recurrent neural network, the multi-label classifier involves a novel iterative sequential model with an attention module. We show that our scanpath predictor generates human-like visual scanpaths. We also demonstrate that the use of artificial visual scanpaths improves multi-class multi-label disease classification results on CXR images. The above observations are made from experiments involving around 0.2 million CXR images from 2 widely-used datasets considering the multi-label classification of 14 pathological findings. Code link: https://github.com/ashishverma03/SDC
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