ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
This work addresses the problem of accurate and accessible diagnosis of thorax diseases in smart healthcare, representing an incremental improvement over existing deep learning methods.
The authors tackled the challenge of diagnosing thoracic diseases from chest radiographs by proposing ChestNet, a deep neural network that integrates an attention mechanism to focus on pathological regions, achieving superior performance over three state-of-the-art models on the Chest X-ray 14 dataset without using extra training data.
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.