基于可逆移动倒瓶颈的内存高效三维U-网脑肿瘤分割
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation
摘要：
我们建议将内存节省技术与传统的U-Net架构相结合，以增加脑肿瘤分割（BraTS）挑战模型的复杂性。BraTS挑战包括将240 x240 x155 x4输入图像3D分割为一组肿瘤类别。由于体积大，需要3D卷积层，因此该任务非常占用内存。为了解决这个问题，以前的方法使用较小的裁剪图像，同时限制模型的深度和宽度。我们的3D U-Net使用MobileNetV 2，MnasNet和最近的EfficientNet架构中定义的移动反向瓶颈块的可逆版本，以在训练期间节省激活内存。使用可逆层使模型能够在给定该层输出的情况下重新计算输入激活，通过消除在前向传递期间存储激活的需要来节省内存。反向残差瓶颈块使用轻量级的dependency可分离卷积，通过将卷积分解为逐点卷积和dependency卷积来减少计算。此外，该块通过在输入和输出线性1x 1卷积之间放置中间扩展层来反转传统的瓶颈块，从而减少通道总数。给定固定的内存预算，通过这些内存节省技术，我们能够训练高达3倍大的图像体积，深度增加25%的模型，或者通道数量高达2倍的模型。
英文摘要：
We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The BraTS challenge consists of a 3D segmentation of a 240x240x155x4 input image into a set of tumor classes. Because of the large volume and need for 3D convolutional layers, this task is very memory intensive. To address this, prior approaches use smaller cropped images while constraining the model's depth and width. Our 3D U-Net uses a reversible version of the mobile inverted bottleneck block defined in MobileNetV2, MnasNet and the more recent EfficientNet architectures to save activation memory during training. Using reversible layers enables the model to recompute input activations given the outputs of that layer, saving memory by eliminating the need to store activations during the forward pass. The inverted residual bottleneck block uses lightweight depthwise separable convolutions to reduce computation by decomposing convolutions into a pointwise convolution and a depthwise convolution. Further, this block inverts traditional bottleneck blocks by placing an intermediate expansion layer between the input and output linear 1x1 convolution, reducing the total number of channels. Given a fixed memory budget, with these memory saving techniques, we are able to train image volumes up to 3x larger, models with 25% more depth, or models with up to 2x the number of channels than a corresponding non-reversible network.
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