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

arXiv: https://arxiv.org/abs/1807.03058 · HTML: https://mlsanity.com/paper/1807.03058/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation](https://mlsanity.com/paper/2203.05573/) (2022)
- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs](https://mlsanity.com/paper/2201.07344/) (2022)
- [Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition](https://mlsanity.com/paper/2307.10036/) (2023)
- [Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture](https://mlsanity.com/paper/2404.11843/) (2024)
- [DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification](https://mlsanity.com/paper/2505.23595/) (2025)
- [Understanding Human Innate Immune System Dependencies using Graph Neural Networks](https://mlsanity.com/paper/2108.02872/) (2021)
- [Intelligent computational model for the classification of Covid-19 with chest radiography compared to other respiratory diseases](https://mlsanity.com/paper/2108.05536/) (2021)
- [A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification](https://mlsanity.com/paper/2203.15060/) (2022)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

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.