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

By Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, Yi Yang. 2018

289 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “Therefore, the research on the interpretability of deep learning for medical image segmentation [145] [146] [147] [148] will be a popular direction in future.”
Computer-aided detection in chest radiography based on artificial intelligence: a survey (2018, methodology): “[111] proposed the attention guided convolutional neural network (AG-CNN), which has three branches, i.e., global branch, local branch, and fusion branch.”
PadChest: A large chest x-ray image dataset with multi-label annotated reports (2019, background): “[6] proposed the attention guided CNN to help combine global and local information in order to improve recognition performance.”
A Survey on Vision Transformer (2020, unknown): “AG-CNN [42] proposes to crop a sub-region from global image by the attention heat map for medical image classification.”
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “In such cases, in order to improve the accuracy, current frameworks (Guan et al., 2018; Khened et al., 2018; Roth et al., 2017, 2018) rely on additional preceding object localisation models to simplify the task into separate localisation and subsequent classification/segmentation steps, or guide…”
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison (2019, methodology): “Due to the introduction of this large dataset, substantial progress has been made towards developing automated chest radiograph interpretation models (Yao et al. 2017; Rajpurkar et al. 2017; Li et al. 2017b; Kumar, Grewal, and Srivastava 2018; Wang et al. 2018; Guan et al. 2018; Yao et al. 2018).”
Attention mechanisms in computer vision: A survey (2021, background): “[97, 46, 139, 70, 128, 36, 11, 156] , [137, 143, 63, 161, 159, 6, 177, 80] , [67, 180, 10, 23, 24, 163, 155, 151] , [44, 49, 106, 64, 168, 18, 11, 36] , [48, 16, 169, 153, 174, 127, 53, 92] , [89, 21, 5, 134, 65, 171, 2, 109] , [45, 82, 157, 91, 100, 129, 146, 131] , [138, 85, 175, 76, 166, 133, 167”