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SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structur...
The proposed SSIMLayer is a new nonlinear computational layer of high learning capacity to the deep convolutional neural network architectures. -
RSNA MICCAI PNG
The RSNA-MICCAI PNG dataset is a dataset for training deep learning models on mpMRI scans. -
Test dataset for Hybrid GNN approach
The dataset is used for testing the proposed hybrid GNN approach. -
Hybrid GNN approach for predicting node data for 3D meshes
The dataset is used for training a hybrid GNN approach for predicting node data for 3D meshes. -
ICML archive
The dataset used in the paper is the ICML archive, which contains papers and code from the ICML conferences. -
NeurIPS archive
The dataset used in the paper is the NeurIPS archive, which contains papers and code from the NeurIPS conferences. -
Prostate-3T dataset
The Prostate-3T dataset is used for prostate segmentation from MRI images. -
Messidor-2
The dataset used for the diabetic retinopathy diagnosis and uncertainty quantification task. -
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promis- ing deep learning methods have recently been... -
STAR-QSM dataset
A dataset of QSM images reconstructed using the STAR-QSM method, used for comparison with the autoQSM method. -
QSMnet dataset
A dataset of QSM images reconstructed using the QSMnet method, used for comparison with the autoQSM method. -
autoQSM dataset
A dataset of 209 healthy subjects with ages ranging from 11 to 82 years old, used for training a deep neural network for QSM reconstruction without brain extraction. -
Functional connectivity patterns of autism spectrum disorder identified by de...
Autism spectrum disorder (ASD) is regarded as a brain disease with globally disrupted neuronal networks. Functional connectivity in ASD has not reached a consensus of the... -
GRU-AE Model Training Data
GRU-AE model training data -
Waste Classification using Computer Vision and Deep Learning
Dataset for waste classification using computer vision and deep learning -
Deep Neural Networks
Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech... -
Android Malware Detection using Autoencoder
The dataset used in this paper for Android malware detection using Autoencoder
