Despite many recent advances, computer-aided mild cogni- tive impairment (MCI) conversion prediction is still a very challenging task due to: 1) the abnormal areas are subtle compared to the size of the whole brain, 2) the features’ dimension is much larger than the number of samples. To tackle these problems, we propose a region ensemble model using a divide and conquer strategy to capture the disease’s finer rep- resentation. Specifically, the features are independently extracted from non-overlapping regions and then fused to describe the subject accord- ing to the attention scores. Moreover, we design a novel loss that models the relationship between different stages of the disease to regularize the training process explicitly. Experiments on public data sets for MCI con- version prediction demonstrate that our method has achieved state-of- the-art performance. Specifically, the area under the receiver operating characteristic curve (AUC) is improved from 79.3% to 85.4%. Beyond that, each region’s contribution can be assessed quantitatively, using the proposed method.