564 datasets found

Focal cortical dysplasia (FCD) dataset for automatic detection of FCD on magnetic resonance images (MRI)

The dataset used in the paper is CIFAR-10, CIFAR-100, and Tiny-Imagenet.

The dataset contains protest images from South Korea and Hong Kong.

Image data provide unique information about political events, actors, and their interactions which are difficult to measure from or not available in text data.

Particle track reconstruction based on deep learning methods

The dataset used in this paper for survival analysis with deep learning models.

The LEMNA dataset is a dataset of explanations for deep learning models in security.

The proposed C-DNN receiver based on data driven deep learning for underwater acoustic chirp communications.

Single Deep CFR (SD-CFR) is a variant of Deep CFR that has a lower overall approximation error by avoiding the training of an average strategy network.

Precise user modeling is critical for online personalized recommen-der services. Generally, users’ interests are diverse and are not limited to a single aspect, which is...

Amazon book data set contains book reviews and metadata from Amazon. Following previous work, we regard reviews as positive samples and randomly select products not rated by a...

The production data set collected from Alibaba display advertising platform. We randomly select about 1% of 1-day samples for training and 0.1% of the next-day samples for...

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A dataset of brain metastases (BM) stereotactic radiosurgery (SRS) MRI datasets.

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A dataset of brain metastases (BM) autosegmentation, including T1 contrast enhanced (T1CE) MRI datasets from six different centers.

Stochastic Deep CDL on 6 minutes of MEG data with 204 channels and a sampling rate of 150Hz.

A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

The dataset used in the paper is a cloud resolving model dataset.

A large-scale clinical EEG dataset for Deep Learning