564 datasets found

The dataset used in this paper is the SqueezeNet v1.1 and ZynqNet CNNs, which are characterized by small model size and limited computational requirements.

Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer...

A successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data.

CS-150 is a dataset consisting of 150 papers and includes 1175 images.

DSSE-200 is a complex document layout dataset including various dataset styles.

Document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The dataset used in the paper is a collection of experiences sampled from a replay buffer, used to train and evaluate the proposed Multi-step DDPG (MDDPG) and Mixed Multi-step...

The fastMRI AXT1 multi-coil brain dataset is consisted of 248 training volumes (3844 slices) and 82 validation (1424 slices) validation volumes.

Building Data Genome 2.0 (BDG2.0) dataset comprises hourly time-series data from electrical meters, sourced from the BDG2 project, for modeling purposes.

Deep learning and insomnia: assisting clinicians with their diagnosis

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The dataset used in this study for brain sMRI analysis, which includes seven public datasets, including one dataset for pre-training and other six datasets across three diseases...

The Imbalanced Gradients dataset is a benchmark for evaluating the robustness of deep neural networks.

Kinect-based gait anomaly detection dataset.

A dataset of Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data used to predict suicidal ideation.

The AlexNet dataset is used to test the proposed systematic weight pruning framework.

The LeNet-5 dataset is used to test the proposed systematic weight pruning framework.

The CrackMap dataset is a new publicly available dataset for road crack segmentation.

COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant.