Abstract
Despite many recent advances, computer-aided mild cognitive 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 representation. Specifically, the features are independently extracted from non-overlapping regions and then fused to describe the subject according 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 conversion 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.
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Acknowledgments
This work has been supported by the National Key Research and Development Program Grant 2018AAA0100400, the National Natural Science Foundation of China (NSFC) grants 61773376, 61836014, 61721004 and 31870984.
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Zhao, YX., Zhang, YM., Song, M., Liu, CL. (2021). Region Ensemble Network for MCI Conversion Prediction with a Relation Regularized Loss. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_18
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