ChestNet：一种用于胸部X线摄影疾病分类的深层神经网络
ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
摘要：
计算机辅助技术可使胸部X线摄影对胸部疾病的诊断更准确、更容易。尽管基于深度学习的解决方案取得了成功，但这一任务仍然是智能医疗中的一个主要挑战，因为它本质上是一个弱监督学习问题。在本文中，我们将注意力机制融入到深度卷积神经网络中，从而提出了胸部网络模型，以解决胸部X线摄影中胸部疾病的有效诊断。该模型由两个分支组成：分类分支作为统一的特征提取-分类网络，使用户从麻烦的手工特征提取中解脱出来;注意分支利用类别标签与病理异常位置之间的相关性，使模型能够自适应地集中于病理异常区域。我们使用官方的患者分割，在胸部X射线14数据集上评估了我们的模型与三种最先进的深度学习模型。结果表明，该模型在诊断胸片上的14种胸部疾病时，优于不使用额外训练数据的其他方法。
英文摘要：
Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. Despite the success of deep learning-based solutions, this task remains a major challenge in smart healthcare, since it is intrinsically a weakly supervised learning problem. In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography. This model consists of two branches: a classification branch serves as a uniform feature extraction-classification network to free users from troublesome handcrafted feature extraction, and an attention branch exploits the correlation between class labels and the locations of patholog-ical abnormalities and allows the model to concentrate adaptively on the patholog-ically abnormal regions. We evaluated our model against three state-of-the-art deep learning models on the Chest X-ray 14 dataset using the official patient-wise split. The results indicate that our model outperforms other methods, which use no extra training data, in diagnosing 14 thorax diseases on chest radiography.
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。
