# Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

arXiv: https://arxiv.org/abs/1801.09927 · HTML: https://mlsanity.com/paper/1801.09927/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 30 of 36)

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- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis](https://mlsanity.com/paper/2208.09282/) (2022)
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- [Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs](https://mlsanity.com/paper/2107.06563/) (2021)
- [Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning](https://mlsanity.com/paper/2101.08992/) (2021)
- [A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification](https://mlsanity.com/paper/2212.03411/) (2022)
- [BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition](https://mlsanity.com/paper/2302.11082/) (2023)
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- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers](https://mlsanity.com/paper/2401.06349/) (2024)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification](https://mlsanity.com/paper/2505.23595/) (2025)
- [Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning](https://mlsanity.com/paper/2103.02982/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [Finding Strong Gravitational Lenses Through Self-Attention](https://mlsanity.com/paper/2110.09202/) (2021)
- [Do it Like the Doctor: How We Can Design a Model That Uses Domain Knowledge to Diagnose Pneumothorax](https://mlsanity.com/paper/2205.12159/) (2022)
- [ARTEMIS: Using GANs with Multiple Discriminators to Generate Art](https://mlsanity.com/paper/2311.08278/) (2023)
- [Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping](https://mlsanity.com/paper/2408.15947/) (2024)
- [A distillation based approach for the diagnosis of diseases](https://mlsanity.com/paper/2108.03470/) (2021)
- [A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts](https://mlsanity.com/paper/2201.07798/) (2022)
- [Comparative Evaluation of Digital and Analog Chest Radiographs to Identify Tuberculosis using Deep Learning Model](https://mlsanity.com/paper/2307.14859/) (2023)
- [More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosis](https://mlsanity.com/paper/2408.02214/) (2024)
- [Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis](https://mlsanity.com/paper/2504.15562/) (2025)
- [Learning Sparse Label Couplings for Multilabel Chest X-Ray Diagnosis](https://mlsanity.com/paper/2511.07801/) (2025)

## Abstract (arXiv)

This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. The Comprehensive experiment is conducted on the ChestX-ray14 dataset. We first report a strong global baseline producing an average AUC of 0.841 with ResNet-50 as backbone. After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community.