1. A method for anatomical object detection in a medical image of a patient, comprising: receiving a medical image of a patient including a target anatomical object; and detecting a pose of the target anatomical object in the medical image in a series of marginal parameter spaces of increasing dimensionality using a respective trained deep neural network for each of the marginal parameter spaces, wherein each respective trained deep neural network inputs image patches of the medical image, samples pixels or voxels in each input image patch in a sparse sampling pattern adaptively learned during training of the respective trained deep neural network, and operates directly on the pixels or voxels sampled from each input image patch to calculate one or more candidates for the target anatomical object in a respective one of the marginal parameter spaces and wherein, for each of the marginal parameter spaces, a respective cascade of trained shallow sparse neural networks filters image patches of the medical image to remove negative image patches prior to the remaining image patches being input to the respective trained deep neural network for that marginal parameter space.