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.

本文提出了 ChestNet 模型，它将注意力机制融合到深度卷积神经网络中，以有效诊断胸部疾病，其结果表明，在使用官方病人方式分割的 Chest X-ray 14 数据集上，该模型优于其他无需额外训练数据使用的方法。