Fig. 1
From: Using autoencoders as a weight initialization method on deep neural networks for disease detection
Overall pipeline of our experiments. This figure illustrates the chosen metodology for our work. Firstly, we pre-train the autoencoders (AEs), before embedding them to the top layers of the classification network, fullfilling either Strategy 1 (import only the encoding layers from the AE) or Strategy 2 (import the complete AE). Each of the full assembled architectures is then trained to detect one of the 5 cancer types, in the input data. The training process can follow two different approaches, regarding the imported weights of the AEs: (A) fixing them or (B) allowing subsequent fine-tune. I represents the input layer, E the encoding layer, \(\hat {I}\) the output layer of the AE; at the classification region of the network, D represents the fully connected layer, and O the output of the classifer