1. A method for anatomical object detection in a 3D medical image of a patient, comprising: receiving a 3D medical image of a patient including a target anatomical object; and detecting a 3D pose of the target anatomical object in the 3D medical image in a series of marginal parameter spaces of increasing dimensionality using a respective trained sparse deep neural network for each of the marginal parameter spaces, wherein each respective trained sparse deep neural network inputs hypothesis image patches in the respective marginal parameter space, samples voxels in each hypothesis image patch in a sparse sampling pattern adaptively learned during training of the respective trained sparse deep neural network, and calculates, for each hypothesis image patch, a probability that the hypothesis image patch is an image patch of the target anatomical object in the respective marginal parameter space based on the voxels sampled in the hypothesis image patch in the sparse sampling pattern.