Fig. 1. A conceptual diagram of representation learning and auto-encoder. A: Representation learning implies training an encoder to automatically map into the representations space, z, needed for detection or classification from input modality (e.g., X-ray images), x, where z is a vector in the latent space with smaller dimensions than those of input modality, x. This model was trained to differentiate X-ray images of different body parts, including those of the head, chest, abdomen, spine, pelvis, and upper and lower extremities. B: Auto-encoder is mainly composed of two components: an encoder and a decoder. The encoder maps the input modality into a latent vector, z, and then, based on the vector z, the decoder generates a novel sample of the target modality.