Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation 不同于GAN,对于此类迁移学习的目标是使得原始数据和目标数据的特征没有差别。原始标签是0.目标是1,也就是D输出的概率大就判别为目标,对于前项,需要让他更接近于目标,所以要让D(SNET-S)变大,让D(SNET-T变小)。同时这个损失...
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the...
To tackle the above challenges, in this paper, we propose a\nboundary-weighted domain adaptive neural network (BOWDA-Net). To make the\nnetwork more sensitive to the boundaries during segmentation, a\nboundary-weighted segmentation loss (BWL) is proposed. Furthermore, an advanced\nboundary-...
In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context featu...
Network initialization.与ANN相比,SRNN需要初始化权重和脉冲神经元的超参数(即神经元类型、时间常数、阈值、起始电位)。我们使用补充表1中给出的每层特定参数随机初始化时间常数,遵循严格的正态分布(μ, σ)。对于所有神经元,膜电位的起始值用在范围内均匀分布的随机值初始化 [0,θ]。网络的偏置权重初始化为零,...
In deep neural networks, the feature vector x usually comprises the activations after a certain layer. Let us de- note by f the network that produces x. To align the two domains, we therefore need to enforce the networks f to output feature vectors that minimize the domain distance dH(S...
(\mathrm{ConvA}\)(i.e., local consistency network). It adopts the GCN model proposed by Kipf16. We briefly describe\(ConvA\)as a deep feedforward neural network. Input a feature set X and an adjacency matrix A, and output the embedding Z of the i-th hidden layer of the network as...
为了验证SASAAN模型的诊断性能,分别引入了DCNN, 深度域混淆网络(deep domain confusion, DDC),深度域自适应网络(deep adaptation network, DAN),深度域对抗神经网络(domain-adversarial neural networks, DANN)和SAAN这5种方法进行比较。不同方法针对4种多工况迁移任务的平均诊断精度如表5所示。由于DCNN未建立源域与目...
through which it can be seen that the values of MAE and RMSE with the addition of the DNN structure are smaller than those without the DNN structure, verifying the superiority of the model proposed in this paper and reflecting the fact that in general, shallow neural network structures cannot...
& Gao, Y. Independently recurrent neural network (indrnn): building a longer and deeper RNN. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5457–5466 (IEEE, 2018). Arjovsky, M., Shah, A. & Bengio, Y. Unitary evolution recurrent neural networks. In ...