57–59 The disorder is characterized by diastolic dysfunction related to a marked increase in myocardial stiffness resulting in impaired ventricular filling; most cases are thought to be idiopathic. Presenting symptoms are nonspecific and primarily respiratory. Occasionally, the diagnosis is made after a...
While the bounding boxes of emphysema were large and well-defined, which led to a high degree of overlap with the GT bounding boxes, the performance achieved for emphysema was not affected by the IoU-T. It is encouraging that the F1-scores of most abnormalities obtained by the YOLOX model...
We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall ...
In the present study, we found the B6/JVS mouse also develops a similar cardiomegaly and also responds to carnitine therapy. In short, this study clearly demonstrates that a mouse with a different background, if only the mutant gene is defective, is capable of exhibiting cardiomegaly and has...
The postero-anterior (PA) chest radiograph is a common and non-invasive way to radiologically assess the size of the heart for disease and patient response to treatment [1]. There are generally three methods for assessing the size of the heart on a plain chest radiograph. The two most ...
Recently, transfer learning, a method of applying algorithms implemented in a specific environment to other similar fields, has demonstrated high efficiency and excellent performance, and is thus being used in many areas [16]. Among various deep learning algorithms, the structural characteristics of ...
Deep learning-based methods are being used to identify the chest anatomy segmentation, but most of them only consider the lung segmentation, requiring a great deal of training. This work is based on a multiclass concatenation-based automatic semantic segmentation network, CardioNet, that was ...
Deep learning-based methods are being used to identify the chest anatomy segmentation, but most of them only consider the lung segmentation, requiring a great deal of training. This work is based on a multiclass concatenation-based automatic semantic segmentation network, CardioNet, that was ...