论文标题
带双头网络的胸部X光片中的多任务肺结节检测
Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network
论文作者
论文摘要
肺结节可能是潜在肺癌的令人震惊的前体。在胸部射线照相分析过程中，缺点检测仍然是胸腔放射学家的普遍挑战。在这项工作中，我们提出了一种用于胸部X光片分析的多任务肺结节检测算法。与过去的方法不同，我们的算法预测了一个全球级别标签，指示结节的存在以及局部级别标签，并使用双头网络（DHN）预测结节位置。我们证明了与常规方法相比，多任务公式产生的有利的结节检测性能。此外，我们还引入了针对DHN量身定制的新型双重增强（DHA）策略，并在进一步增强全球和局部结节预测方面展示了其重要性。
Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge among thoracic radiologists. In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis. Unlike past approaches, our algorithm predicts a global-level label indicating nodule presence along with local-level labels predicting nodule locations using a Dual Head Network (DHN). We demonstrate the favorable nodule detection performance that our multi-task formulation yields in comparison to conventional methods. In addition, we introduce a novel Dual Head Augmentation (DHA) strategy tailored for DHN, and we demonstrate its significance in further enhancing global and local nodule predictions.
