Skip to main content
Fig. 1 | BMC Medical Informatics and Decision Making

Fig. 1

From: Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

Fig. 1

The automatic segmentation and diagnosis framework for pneumothorax on chest X-rays. a The proposed segmentation network architecture. The difference between our segmentation network and the original FC-DenseNet is marked in red on the subgraph. b An example of a dense block embedded with scSE modules. c A layer in the scSE-embedded dense block that consists of batch normalization, exponential linear unit, \(3\times 3\) convolution operation, and drop-out rate \(\rho =0.2\). d A transition down block, which is composed of batch normalization, exponential linear unit, \(1\times 1\) convolution, dropout (\(\rho =0.2\)) and \(2\times 2\) max pooling. (e) A transition up block, which is composed of \(3\times 3\) transposed convolution

Back to article page