ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

TL;DR

ChestNet is a deep neural network that improves thoracic disease classification on chest X-rays by integrating attention mechanisms, enabling better focus on abnormal regions and outperforming existing models without extra data.

Contribution

This paper introduces ChestNet, a novel deep learning model with attention mechanisms for improved thorax disease diagnosis on chest radiographs.

Findings

Outperforms state-of-the-art models on Chest X-ray 14 dataset

Effectively localizes pathological abnormalities

No additional training data required

Abstract

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.