To address this concern, we introduce EMCAD, a new efficient multi-scale convolutional attention decoder, designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale
The system was initially developed for the segmentation of brain lesions in MRI scans. It was employed for our research presented in [1],[2], where a 3D network architecture with two convolutional pathways was presented for the efficient multi-scale processing of multi-modal MRI volumes. If th...
It was employed for our research presented in [1],[2], where a 3D network architecture with two convolutional pathways was presented for the efficient multi-scale processing of multi-modal MRI volumes. If the use of the software positively influences your endeavours, please cite [1]. [1] ...
If deep convolutional neural networks are an approximation of biological vision, it should be possible to localize object-like regions at lower resolution and recognize them by zooming on them at higher resolution similar to the way our peripheral vision is coupled with foveal vision. To this end...
并行扩张卷积层(parallel dilated convolutional layer) 其实就是不同膨胀因子的空洞卷积,并行运算后concat。如下图所示 参考链接 空洞卷积 在这里插入图片描述 用$f_{in}^i$代表第i次迭代的扩张卷积层,逐步更新迭代并行空洞卷积层输出特征图$f_{out}^i$: 在这里插入图片描述 上式中,$\theta_C^k$和$b_C^...
How to perform multi-scale context aggregation within limited computation budget is important. In this paper, firstly, we introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP). It is a lightweight cascaded structure for Convolutional Neural Networks...
use computation between the classifiers, we incorporate them asearly-exits into a single deep convolutional neural networkand inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and...
The deployment of deep convolutional neural networks (CNNs) in many Real-World applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; ...
解决:Multi-scale features maps 让所有的分类器仅使用coarse-level features,在特定层的feature map 通过concatenate一个或两个卷积来进行计算,包括两种情况:一是对于将常规卷积应用于前一层的相同scale特征上的结果(Figure2中水平连接)二是对于前一层对fine-sale的特征图应用跨步卷积的结果(Figure2中对角线连接)。水...
scale. To achieve an efficient mix, we exploit the domain-wide receptive field provided by self-attention for regionalscale mixing and convolutional kernels restricted to local scale for local-scale mixing. More specifically, our proposed method mixes regional features associated with local features ...