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

Abstract

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.

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
• Learning what to look in chest X-rays with a recurrent visual attention model

Cited by in corpus (16)

• Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
• Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
• Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study
• Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
• Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study
• Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
• Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
• A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
• Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation
• Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
• Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning
• Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System
• A distillation based approach for the diagnosis of diseases
• Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening