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Found 8 papers, 1 papers with code
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances.
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data.
Alternating Segmentation and Simulation for Contrast Adaptive Tissue Classification
In this work, we propose a new framework for supervised segmentation approaches that is robust to contrast differences between the training MR image and the input image.
Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks
Accurate PET image reconstruction requires attenuation correction, which is based on the electron density of tissues and can be obtained from CT images.
TBI Contusion Segmentation from MRI using Convolutional Neural Networks
In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI.
Classifying magnetic resonance image modalities with convolutional neural networks
The proposed CNN automatically identifies the MR contrast of an input brain image volume.
Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks
In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images.
