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Found 9 papers, 1 papers with code
Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources
This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch.
Multi-stages attention Breast cancer classification based on nonlinear spiking neural P neurons with autapses
Downsampling in deep networks may lead to loss of information, so for compensating the detail and edge information and allowing convolutional neural networks to pay more attention to seek the lesion region, we propose a multi-stages attention architecture based on NSNP neurons with autapses.
SLP-Net:An efficient lightweight network for segmentation of skin lesions
Prompt treatment for melanoma is crucial.
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture
To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN.
MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection
We first formulate the training and parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters.
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks
This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH).
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Integrating Tensor Similarity to Enhance Clustering Performance
The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix.
