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. 2021 Oct 6;3(6):e210014. doi: 10.1148/ryai.2021210014

Figure 1:

Training from scratch: only the data in the target domain is used for training. Transfer learning: the weights of a pretrained network at the source domain (Ds) are fine-tuned with the target domain data (Dt). Off-the-shelf deep features: the pretrained network is used as a feature extractor on the target domain data, and a traditional machine learning algorithm such as support vector machine (SVM) uses the extracted features to classify the imaging findings. CXRs = chest radiographs, FC = fully connected layer.

Training from scratch: only the data in the target domain is used for training. Transfer learning: the weights of a pretrained network at the source domain (Ds) are fine-tuned with the target domain data (Dt). Off-the-shelf deep features: the pretrained network is used as a feature extractor on the target domain data, and a traditional machine learning algorithm such as support vector machine (SVM) uses the extracted features to classify the imaging findings. CXRs = chest radiographs, FC = fully connected layer.