487 datasets found

Cryo-CARE: a method for content-aware image restoration for cryo-transmission electron microscopy data

Deep learning and insomnia: assisting clinicians with their diagnosis

The dataset used for knee menisci segmentation and relaxometry in 3D UTE cones MR imaging using attention U-Net with transfer learning

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

RevColV2 is a new architecture that learns disentangled representations during MIM pre-training and keeps a unified autoencoder architecture when transferring to downstream tasks.

ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators

This paper presents DADLNet, a dynamic domain adaptation based deep learning network framework for decoding motor imagery tasks.

The proposed architecture uses a CNN-based approach to detect COVID-19 from chest X-ray images. The dataset is used to train and evaluate the proposed model.

COVID-19 CT dataset for predicting COVID-19 from chest CT images using attentional convolutional network.

Chest X-ray images dataset used for COVID-19 severity classification

A small dataset of 65 panoramic images for automatic detection of atherosclerotic carotid plaques.

Mixture-of-Experts (MoE) architecture, conditional computing, cross-token modeling, Sparse-MLP model

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical...

Convolutional Neural Network (CNN) has demonstrated its success in plentiful computer vision application, but typically accompanies high computation cost and numerous redundant...

A semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation.

The proposed TransDepth framework for pixel-wise prediction problems involving continuous labels.

The dataset used in this work consists of 9 axial volumes from Radiopaedia consisting of both positive and negative COVID indications.

The dataset used in this work consists of 20 volumes from Jun et al. consisting of infections labelled by two radiologists and verified by another experienced radiologist.

The dataset used in this work consists of 29 CT volumes from two different sources of lung infection data, resulting from COVID-19.

Ultrasound Localization Microscopy can resolve the microvascular bed down to a few micrometers. To achieve such performance microbubble contrast agents must perfuse the entire...