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European Radiology Experimental
Fig. 3 | European Radiology Experimental

Fig. 3

From: Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images

Fig. 3

The neural network architecture with DenseNet-121 as the frozen base model. Individual feature vectors were averaged and fed to the three-layer classification network. Lungs were processed separately, and the outputs were either taken individually or combined by taking the maximum of the positive class. Note that only the parameters trained by gradient descent were frozen in the base model, i.e., the batch statistics (the running mean and variance in the batch normalisation layers in the base model) were calculated during fine-tuning. PE, Pulmonary embolism

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