Diseases Detection On Chest X-RaysWith Deep Learning

Table 1. Sizes of convolutional kernels for DenseNets121 architectures. Note that each conv layer shown in the table corresponds to the sequence BN-ReLU-Conv.
Figure 1. The schema of the original Inception module (left) and the SE-Inception module (right).
Table 2. Sizes of convolutional kernels for SE-DenseNets121 architectures. Note that each conv layer shown in the table corresponds to the sequence BN-ReLU-Conv.
Figure 2. The disease distribution in the ChestX-ray14 dataset.
Figure 3. The disease distribution in downsized ChestX-ray14 dataset
Table 3. Our implementation outperforms the CheXNet published results on 14 pathologies’ mean AUROC in the original ChestXray14 dataset.
Table 4. pre-trained DenseNet121 outperforms the other pre-trained models’ results on 14 pathologies’ mean AUROC in the downsized ChestX-ray14 dataset.
Table 5. DenseNet121 without pre-train outperforms SEDenseNet121 non-pretrained models’ results on 14 pathologies’ mean AUROC in the downsized ChestX-ray14 dataset.
Figure 4. Patient with congestive heart failure and cardiomegaly (enlarged heart).
Figure 5. Patient with a large right pleural effusion (fluid in the pleural space).

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Wang Meijie

Wang Meijie

https://meijiewang.tech

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