A method for detecting target tissue in medical image data, comprising: inputting medical image data to a variational autoencoder configured to reduce a dimensionality of the medical image data to a latent space having one or more latent variables with latent variable values, such that the latent variable values corresponding to an image with no tissue of a target tissue type fit within one or more clusters, wherein the training image data for the variational autoencoder includes only image information obtained from normal, healthy subjects, who do not have any tissue of the target tissue type; and the clusters are obtained by clustering the latent variable values of the training image data in the latent space; and wherein test images of a subject are input to an encoder network of the trained variational autoencoder to generate the latent variable values corresponding to the input test image in two manifolds (232, 234), wherein the manifolds (232, 234) are a part of the latent space and correspond to two latent variables, wherein the latent variables represent parameters of the tissue, wherein the encoder network learns the parameters of distribution of a set of latent variables and a decoder network reconstructs the input images from the latent variables; determining a probability that the latent variable values corresponding to the medical image data fit within the one or more clusters obtained based on the latent variable values of training image data for the variational autoencoder, the probability being determined from the latent variable values for the medical image output by the encoder network relative to the clusters of the latent variable values of the training image data for the variational autoencoder; wherein the training image data includes image information obtained from normal, healthy subjects, who do not have any tissue of the target tissue type; and determining that a tissue of the target tissue type is present in response to a determination that the medical image data have less than a threshold probability of fitting within any of the one or more clusters based on the latent variable values. inputting medical image data to a variational autoencoder configured to reduce a dimensionality of the medical image data to a latent space having one or more latent variables with latent variable values, such that the latent variable values corresponding to an image with no tissue of a target tissue type fit within one or more clusters, wherein the training image data for the variational autoencoder includes only image information obtained from normal, healthy subjects, who do not have any tissue of the target tissue type; and the clusters are obtained by clustering the latent variable values of the training image data in the latent space; and wherein test images of a subject are input to an encoder network of the trained variational autoencoder to generate the latent variable values corresponding to the input test image in two manifolds (232, 234), wherein the manifolds (232, 234) are a part of the latent space and correspond to two latent variables, wherein the latent variables represent parameters of the tissue, wherein the encoder network learns the parameters of distribution of a set of latent variables and a decoder network reconstructs the input images from the latent variables; wherein the training image data for the variational autoencoder includes only image information obtained from normal, healthy subjects, who do not have any tissue of the target tissue type; and the clusters are obtained by clustering the latent variable values of the training image data in the latent space; and wherein test images of a subject are input to an encoder network of the trained variational autoencoder to generate the latent variable values corresponding to the input test image in two manifolds (232, 234), wherein the manifolds (232, 234) are a part of the latent space and correspond to two latent variables, wherein the latent variables represent parameters of the tissue, wherein the encoder network learns the parameters of distribution of a set of latent variables and a decoder network reconstructs the input images from the latent variables; determining a probability that the latent variable values corresponding to the medical image data fit within the one or more clusters obtained based on the latent variable values of training image data for the variational autoencoder, the probability being determined from the latent variable values for the medical image output by the encoder network relative to the clusters of the latent variable values of the training image data for the variational autoencoder; wherein the training image data includes image information obtained from normal, healthy subjects, who do not have any tissue of the target tissue type; and determining that a tissue of the target tissue type is present in response to a determination that the medical image data have less than a threshold probability of fitting within any of the one or more clusters based on the latent variable values.