60 datasets found

Deep learning-based dataset for computer-aided detection of medical images

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

A large-scale CT-derived µ-map dataset for training and evaluation of low-count PET attenuation map generation

A population-prior generation machine for generating population prior information for low-count PET attenuation map generation

Elbow fracture diagnosis from X-ray images using medical knowledge-guided deep curriculum learning

A dataset of studies tagged slice-wise by radiologists for training a deep learning algorithm for detection of hemorrhage in CT scans.

A dataset of 40k studies assembled for training a 3D convolution neural network for CT brain hemorrhage classification.

BloodNet is a deep learning architecture designed for optimal triaging of Head CTs, with the goal of decreasing the time from CT acquisition to accurate ICH detection.

Medical head CT-scan imaging has been successfully combined with deep learning for medical diagnostics of head diseases and lesions. A custom dataset was used for this study.

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices.

A dataset of optical coherence tomography scans showing four different retinal conditions.

Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical...

A fine-tuning approach was employed to incorporate information learned from the WMn network to segment thalamic nuclei from CSFn-MPRAGE images, which have poor intra-thalamic...

A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization...

Automated segmentation of vertebrae on lateral chest radiography using deep learning

A new deep learning approach to learn a hierarchy of conditional latent variables that models a population of anatomical segmentations of interest, enables the classification of...

Brain MR dataset for tumor classification and source type classification

A multi-level ConvLSTM model for the automatic segmentation of left ventricle myocardium in infarcted porcine cine MR images

Modern biomedical image analysis using deep learning often encounters the challenge of limited annotated data. To overcome this issue, deep generative models can be employed to...

The dataset used for training and testing deep neural networks-based denoising models for CT imaging.