"Uses of autoencoders relevant to radiological AI include noise reduction and anomaly detection 2."
Expected headings
"Autoencoders in radiology"
"bottleneck: which is the layer that contains the compressed representation of the input data; this is the lowest possible dimensions of the input data"
"Autoencoders generally are data-specific and lossy. "Data-specific" means that they are only able to compress data similar to what they have been trained on. For example, an autoencoder trained on photos of faces would do a rather poor job of compressing photos of flowers, because the features it would learn would be face-specific 4. "Lossy" means that the decompressed outputs will be degraded compared to the original inputs; this differs from lossless compression."
"The idea of autoencoders has been in the neural network literature for decades (LeCun, 1987; Bourlard and Kamp, 1988; Hinton and Zemel,1994) 3. Traditionally, autoencoders were used for dimensionality reduction or feature learning, in other words, to reduce the complexity of data and reveal its internal structure."
"The idea of autoencoders has been in the neural network literature for decades (LeCun, 1987; Bourlard and Kamp, 1988; Hinton and Zemel,1994) 3. Traditionally, autoencoders were used for dimensionality reduction or feature learning, in other words, to reduce the complexity of data and reveal its internal structure."
"There are several different types of autoencoders 6:"