计算机科学
规范化(社会学)
人工智能
端到端原则
人工神经网络
模式识别(心理学)
推论
机器学习
社会学
人类学
作者
Xinzi He,Alan Q. Wang,Mert R. Sabuncu
标识
DOI:10.1007/978-3-031-43993-3_25
摘要
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network, trained on a large dataset without individual sub-task supervision. Because the overall objective is highly under-constrained, we explicitly disentangle geometric-preserving intensity mapping (skull-stripping and intensity normalization) and spatial transformation (spatial normalization). Quantitative results show that our model outperforms state-of-the-art methods which tackle only a single sub-task. Our ablation experiments demonstrate the importance of the architecture design we chose for NPP. Furthermore, NPP affords the user the flexibility to control each of these tasks at inference time. The code and model are freely-available at https://github.com/Novestars/Neural-Pre-processing .
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