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Deep learning-based dataset for computer-aided detection of medical images
Deep learning-based dataset for computer-aided detection of medical images -
FCD dataset for automatic detection of FCD on MRI
Focal cortical dysplasia (FCD) dataset for automatic detection of FCD on magnetic resonance images (MRI) -
CT-Derived µ-Map Dataset
A large-scale CT-derived µ-map dataset for training and evaluation of low-count PET attenuation map generation -
Population-Prior Generation Machine
A population-prior generation machine for generating population prior information for low-count PET attenuation map generation -
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosi...
Elbow fracture diagnosis from X-ray images using medical knowledge-guided deep curriculum learning -
Radnet: Radiologist Level Accuracy Using Deep Learning for Hemorrhage Detecti...
A dataset of studies tagged slice-wise by radiologists for training a deep learning algorithm for detection of hemorrhage in CT scans. -
Deep 3D Convolution Neural Network for CT Brain Hemorrhage Classification
A dataset of 40k studies assembled for training a 3D convolution neural network for CT brain hemorrhage classification. -
Improved ICH Classification Using Task-Dependent Learning
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. -
Convolutional-LSTM for Multi-Image to Single Output Medical Prediction
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. -
Resource-Frugal Classification and Analysis of Pathology Slides Using Image E...
Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices. -
MIDL 2019 – Extended Abstract Track: Uncertainty Quantification in Computer-A...
A dataset of optical coherence tomography scans showing four different retinal conditions. -
Deep ensemble learning for segmenting tuberculosis-consistent manifestations ...
Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical... -
Fine-tuning of WMn network for CSFn-MPRAGE images
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... -
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutio...
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 L...
Automated segmentation of vertebrae on lateral chest radiography using deep learning -
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Mo...
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 Tumors Classification for MR images based on Attention Guided Deep Lear...
Brain MR dataset for tumor classification and source type classification -
Multi-level ConvLSTM for Left Ventricle Myocardium Segmentation
A multi-level ConvLSTM model for the automatic segmentation of left ventricle myocardium in infarcted porcine cine MR images -
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer...
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... -
Low-dose CT image denoising dataset
The dataset used for training and testing deep neural networks-based denoising models for CT imaging.
