This model assists the radiologist to segment lesion and do positive/negative classifications based on lung CT.
Chest CT Dicom series of 1.25mm and 7.25mm.
The diversity of the training data is limited. The evaluation of the model is not on a comprehensive dataset.
Information on training data
The model uses deep neural networks, trained on data from several sources in China in an end-to-end manner.
30% of Training data has slice thickness of 1.25mm and the remain part is in 7.5mm. Half of the data is negative.
For positive data, pneumonia region volume/lung region volume distribution is
Model performance metrics
Accuracy: 0.9 (negative/positive)
Dice score on pneumonia region segmentation: 0.7
Average Dice score on lung lobe segmentation: 0.95
The model has been updated to display pneumonia quantification per lung lobe.
The model structure is the same as before, however, more training data labelled by experienced radiologist/with PCR results are added in.
We increase the ratio of 1mm CT dcm significantly in the Training set for this version.
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