… We proposed a new scale-specific batch normalization layer that applies batch normalization
to the data separately for each input size, resulting in per-size personalized trainable (…

… of 46,754 chest radiographs, of which 30,174 represent normal cases without pneumonia,
16,384 are cases with non-COVID-19 pneumonia and 196 include radiographs of confirmed …

… normal and abnormal frontal chest radiographs, in order to … ) for normal versus abnormal
chest radiograph classification. The … normal and abnormal chest radiographs, thereby providing …

… Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.
In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 590–597 (2019). …

… We use a batch normalization decay rate of 0.9. After every 100 training steps, we calculate
… We sample the batch normalization means and variances by setting the batch normalization …

… [10] and batch normalization [11] to make … chest radiographs is also a challenging task, due
to the complexity and diversity of thorax diseases and the limited quality of chest radiographs…

… This can be provided by imaging modalities, such as chest radiographs (X-ray) or Computed
Tomography (CT) scan images. The radiologists can give an opinion by analyzing and …

… The convolutional block consists of a separable convolution layer, followed by batch
normalization and ReLU non-linearity layers. We added padding to the separable convolutional …

… Chest radiography (CXR) is recommended for triaging at patient presentation and
disease monitoring due to its fast speed, relatively low cost, wide availability, and portability. …

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic
conditions. Being undertaken at primary healthcare centers, they require the presence of …