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Found 28 papers, 3 papers with code
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically.
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image
The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features.
Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
Brain network analysis is essential for diagnosing and intervention for Alzheimer's disease (AD).
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
In this work, a novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages.
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
The proposed model can evaluate characteristics of abnormal brain connections at different stages of Alzheimer's disease, which is helpful for cognitive disease study and early treatment.
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease
In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD.
3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image
A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features.
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis.
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery.
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation
In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e. g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations.
A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions
In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich's helical CT formula to 2D fan-beam CT reconstruction.
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain.
Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases.
Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network
The assistant network and the discriminator are employed to jointly decide whether the segmentation results are real or fake.
A model-guided deep network for limited-angle computed tomography
In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network. We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems.
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body.
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
To the best of our knowledge, the proposed Tensor-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis.
Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus.
High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation
We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images.
Generative Adversarial Network
Image-to-Image Translation
+1
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.
A deep network for sinogram and CT image reconstruction
A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free.
SANet:Superpixel Attention Network for Skin Lesion Attributes Detection
We introduce a superpixel average pooling to reformulate the superpixel classification problem as a superpixel segmentation problem and a SAMis utilized to focus on discriminative superpixel regions and feature channels.
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation.
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one.
