The proposed All-MLP decoder consists of four main steps. First, multi-level features Fi from the MiT encoder go through an MLP layer to unify the channel dimension. Then, in a second step, features are up-sampled to 1/4th and concatenated together. Third, a MLP layer is adopted to fuse the concatenated features F. Finally, another MLP layer takes the fused feature to predict the segmentation mask M with a H 4 × W 4 × Ncls resolution, where Ncls is the number of categories. This lets us formulate the decoder as: Fˆ i = Linear(Ci , C)(Fi), ∀i Fˆ i = Upsample( W 4 × W 4 )(Fˆ i), ∀i F = Linear(4C, C)(Concat(Fˆ i)), ∀i M = Linear(C, Ncls)(F), (4) where M refers to the predicted mask, and Linear(Cin, Cout)(·) refers to a linear layer with Cin and Cout as input and output vector dimensions respectively.翻译
时间: 2023-06-19 18:09:19 浏览: 68
提出的All-MLP解码器包括四个主要步骤。首先,来自MiT编码器的多级特征Fi通过MLP层进行处理,以统一通道维度。然后,在第二步中,特征被上采样到1/4并连接在一起。第三步采用MLP层来融合连接的特征F。最后,另一个MLP层将融合的特征传递给预测分割掩码M,其分辨率为H 4×W 4×Ncls,其中Ncls是类别数。这使我们可以将解码器公式化为:Fˆ i = Linear(Ci , C)(Fi),∀i Fˆ i = Upsample( W 4×W 4 )(Fˆ i),∀i F = Linear(4C, C)(Concat(Fˆ i)),∀i M = Linear(C, Ncls)(F),(4) 这里M是预测的掩码,Linear(Cin,Cout)(·)是具有Cin和Cout作为输入和输出向量维度的线性层。
相关问题
Therefore, this research introduced the combined forecasting to overcome the model selection uncertainty, and proposed GM-GRA-DPC-PSOSVR nonlinear combination for carbon emission forecasting. The combined forecasting binds the information of individual models and meets the requirements of high adaptability and forecasting precision. Furthermore, the combined model GM-GRA-DPC-PSOSVR is suitable for small samples forecasting. The processes of GM-GRA-DPC-PSOSVR are as the following steps:
这段话也没有发现任何语法错误。该段介绍了该研究引入组合预测方法来克服模型选择的不确定性,并提出了GM-GRA-DPC-PSOSVR非线性组合模型用于碳排放预测。组合预测将个体模型的信息绑定在一起,并满足高适应性和预测精度的要求。此外,组合模型GM-GRA-DPC-PSOSVR适用于小样本预测。GM-GRA-DPC-PSOSVR的过程如下所示:
The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.请详细介绍这段话中的技术点和实现方式
这段话主要介绍了一种针对医学图像分割的新型网络结构——频率重新校准U-Net(FRCU-Net)。该网络结构的核心思想是将物体表示为频率,在低数据情况下降低纹理偏差的影响,从而获得更好的泛化性能。具体实现方式包括以下技术点:
1. 使用Laplacian金字塔将物体表示在不同的频率域中,其中高频率负责纹理信息,而较低的频率可能与物体的形状相关。
2. 通过自适应重新校准这些频率表示,以产生更具有区分性的物体表示。为此,首先提出使用通道注意力机制来捕捉一个特征图集合中各通道之间的关系,然后通过一个非线性函数将金字塔各层的特征提取结果组合起来,以得到最终的分割输出。
3. 在ISIC 2017、ISIC 2018、PH2、肺分割和SegPC 2021数据集上对FRCU-Net进行了评估,并与现有的替代方案进行了比较,取得了最先进的结果。
综上所述,FRCU-Net通过将物体表示为频率,以及使用自适应的频率重新校准和通道注意力机制,实现了对低数据情况下医学图像分割的有效处理。