64 datasets found

The ALARM method is a fully automated liver attenuation estimation method that combines deep convolutional neural network (DCNN) and morphological operations.

MRI images of knee joint for cartilage defect diagnosis

A synthetic dataset with controlled modes of variation for lung nodule classification, designed to explore the features learned by a CNN and their effect on out-of-distribution...

The dataset used in this paper for COVID-19 diagnosis from community acquired pneumonia (CAP) in chest computed tomography (CT) images.

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...