The Experts below are selected from a list of 6531 Experts worldwide ranked by ideXlab platform

Qingji Gua - One of the best experts on this subject based on the ideXlab platform.

  • Thorax Disease classification with attention guided convolutional neural network
    Pattern Recognition Letters, 2020
    Co-Authors: Yaping Huang, Zhedong Zheng, Liang Zheng, Qingji Gua, Zhu Zhong, Yi Yang
    Abstract:

    Abstract This paper considers the task of Thorax Disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, Thorax Diseases usually happen in (small) localized areas which are Disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify Thorax Diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer 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. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.

Yi Yang - One of the best experts on this subject based on the ideXlab platform.

  • Thorax Disease classification with attention guided convolutional neural network
    Pattern Recognition Letters, 2020
    Co-Authors: Yaping Huang, Zhedong Zheng, Liang Zheng, Qingji Gua, Zhu Zhong, Yi Yang
    Abstract:

    Abstract This paper considers the task of Thorax Disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, Thorax Diseases usually happen in (small) localized areas which are Disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify Thorax Diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer 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. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.

  • diagnose like a radiologist attention guided convolutional neural network for Thorax Disease classification
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, Yi Yang
    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.

Yaping Huang - One of the best experts on this subject based on the ideXlab platform.

  • Thorax Disease classification with attention guided convolutional neural network
    Pattern Recognition Letters, 2020
    Co-Authors: Yaping Huang, Zhedong Zheng, Liang Zheng, Qingji Gua, Zhu Zhong, Yi Yang
    Abstract:

    Abstract This paper considers the task of Thorax Disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, Thorax Diseases usually happen in (small) localized areas which are Disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify Thorax Diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer 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. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.

  • multi label chest x ray image classification via category wise residual attention learning
    Pattern Recognition Letters, 2020
    Co-Authors: Qingji Guan, Yaping Huang
    Abstract:

    Abstract This paper considers the problem of multi-label Thorax Disease classification on chest X-ray images. Identifying one or more pathologies from a chest X-ray image is often hindered by the pathologies unrelated to the targets. In this paper, we address the above problem by proposing a category-wise residual attention learning (CRAL) framework. CRAL predicts the presence of multiple pathologies in a class-specific attentive view. It aims to suppress the obstacles of irrelevant classes by endowing small weights to the corresponding feature representation. Meanwhile, the relevant features would be strengthened by assigning larger weights. Specifically, the proposed framework consists of two modules: feature embedding module and attention learning module. The feature embedding module learns high-level features with a convolutional neural network (CNN) while the attention learning module focuses on exploring the assignment scheme of different categories. The attention module can be flexibly integrated into any feature embedding networks with end-to-end training. The comprehensive experiments are conducted on the Chest X-ray14 dataset. CRAL yields the average AUC score of 0.816 which is a new state of the art.

  • diagnose like a radiologist attention guided convolutional neural network for Thorax Disease classification
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, Yi Yang
    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.

Qingji Guan - One of the best experts on this subject based on the ideXlab platform.

  • multi label chest x ray image classification via category wise residual attention learning
    Pattern Recognition Letters, 2020
    Co-Authors: Qingji Guan, Yaping Huang
    Abstract:

    Abstract This paper considers the problem of multi-label Thorax Disease classification on chest X-ray images. Identifying one or more pathologies from a chest X-ray image is often hindered by the pathologies unrelated to the targets. In this paper, we address the above problem by proposing a category-wise residual attention learning (CRAL) framework. CRAL predicts the presence of multiple pathologies in a class-specific attentive view. It aims to suppress the obstacles of irrelevant classes by endowing small weights to the corresponding feature representation. Meanwhile, the relevant features would be strengthened by assigning larger weights. Specifically, the proposed framework consists of two modules: feature embedding module and attention learning module. The feature embedding module learns high-level features with a convolutional neural network (CNN) while the attention learning module focuses on exploring the assignment scheme of different categories. The attention module can be flexibly integrated into any feature embedding networks with end-to-end training. The comprehensive experiments are conducted on the Chest X-ray14 dataset. CRAL yields the average AUC score of 0.816 which is a new state of the art.

  • diagnose like a radiologist attention guided convolutional neural network for Thorax Disease classification
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, Yi Yang
    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.

Zhu Zhong - One of the best experts on this subject based on the ideXlab platform.

  • Thorax Disease classification with attention guided convolutional neural network
    Pattern Recognition Letters, 2020
    Co-Authors: Yaping Huang, Zhedong Zheng, Liang Zheng, Qingji Gua, Zhu Zhong, Yi Yang
    Abstract:

    Abstract This paper considers the task of Thorax Disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, Thorax Diseases usually happen in (small) localized areas which are Disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify Thorax Diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer 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. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.