Fig. 2.

Components of the feature extractor in more detail. The backbone network of the feature extractor employs ResNet34, with convolution blocks stacked hierarchically. Utilizing pre-trained models with massively sized datasets such as ImageNet helps extract generalized features, even if it doesn't help extract task-specific features. Additionally, it helps improve the convergence speed of the training. Therefore, all parameters in the feature extractor except the fully connected layer are initialized to the parameters of the model pre-trained with ImageNet. The repetitive operations by the hierarchical architecture encode the features of the input image into an abstract feature vector. Conv Block convolution block, GAP global average pooling, FC fully connected, K kernel size, S stride size, C channel size, PA posteroanterior, Lat lateral