论文标题
具有中间监督机制的有效医疗图像分割
Efficient Medical Image Segmentation with Intermediate Supervision Mechanism
论文作者
论文摘要
由于U-NET的膨胀路径可能忽略了小目标的特征，因此提出了中间监督机制。原始蒙版也将作为中间输出的标签输入网络。但是，U-NET主要参与分割，并且提取的特征也针对分割位置信息，并且输入和输出不同。我们需要的标签是输入和输出都是原始面具，它与重构过程更相似，因此我们提出了另一种中间监督机制。但是，这种中间监测机制的收缩路径提取的特征不一定是一致的。例如，U-NET的收缩路径提取横向特征，而自动编码器则提取纵向特征，这可能会导致扩展路径的输出与标签不一致。因此，我们提出了共享重量解码器模块的中间监督机制。尽管中间监督机制提高了细分精度，但由于额外的输入和多个损失功能，训练时间太长了。对于这些问题之一，我们引入了绑定重量解码器。为了减少模型的冗余，我们将共享权重模块与绑定重量解码器模块相结合。
Because the expansion path of U-Net may ignore the characteristics of small targets, intermediate supervision mechanism is proposed. The original mask is also entered into the network as a label for intermediate output. However, U-Net is mainly engaged in segmentation, and the extracted features are also targeted at segmentation location information, and the input and output are different. The label we need is that the input and output are both original masks, which is more similar to the refactoring process, so we propose another intermediate supervision mechanism. However, the features extracted by the contraction path of this intermediate monitoring mechanism are not necessarily consistent. For example, U-Net's contraction path extracts transverse features, while auto-encoder extracts longitudinal features, which may cause the output of the expansion path to be inconsistent with the label. Therefore, we put forward the intermediate supervision mechanism of shared-weight decoder module. Although the intermediate supervision mechanism improves the segmentation accuracy, the training time is too long due to the extra input and multiple loss functions. For one of these problems, we have introduced tied-weight decoder. To reduce the redundancy of the model, we combine shared-weight decoder module with tied-weight decoder module.
