GANs improve medical image analysis through de-noising, segmentation, and data synthesis.
problem Chronic scarcity of labeled medical images.
method Generative Adversarial Networks (GANs) and their extensions.
result GANs can synthesize realistic medical images to address data scarcity.
Generative model generates synthetic medical images for data augmentation and anonymization.
problem Imbalanced medical imaging data sets, especially for rare pathologies.
method Generative adversarial network (GAN) trained on two public brain MRI datasets.
result Synthetic images improve tumor segmentation performance and serve as an anonymization tool.
MI-GAN generates synthetic medical images for supervised analysis.
problem Lack of large datasets and overfitting in medical image analysis.
method Generative Adversarial Network (GAN) for generating synthetic medical images and masks.
result MI-GAN achieves state-of-the-art performance with dice coefficient of 0.837 on STARE and 0.832 on DRIVE datasets.
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
problem Synthesizing realistic 4D CMR images with annotations and adaptable styles.
method Hybrid GAN approach with XCAT anatomical ground truth and SPADE for semantic preservation.
result Synthesized images with modality-specific features learned from real CMR data.
This study shows how EEG can be used to generate fMRI data.
problem Mapping fMRI from EEG signals.
method Deep learning approaches (Autoencoders, GANs, Pairwise Learning).
result Feasibility of EEG to fMRI brain image mappings.
MAGIC-Flow generates and classifies medical images with interpretability.
problem Challenges in generative modeling for medical imaging.
method Conditional multiscale normalizing flow architecture.
result MAGIC-Flow creates realistic, diverse samples and improves classification.
Bayesian variational inference improves medical image segmentation confidence.
problem Improving interpretability and confidence in deep learning models for medical image segmentation.
method Encoder-decoder architecture based on variational inference for segmenting brain tumor images.
result The model segments brain tumors with both aleatoric and epistemic uncertainty.
Double InfoGAN improves CA by generating clearer, more accurate images.
problem Improper latent variable models in CA lead to sub-optimal solutions.
method Double InfoGAN, a GAN-based method enforcing fundamental assumptions and improving image quality.
result Double InfoGAN outperforms SOTA CA-VAEs in latent separation and image quality.
DDPMs can reproduce medical image context, showing interpolation between samples.
problem Understanding DDPMs' ability to learn spatial context in medical imaging.
method Used stochastic context models (SCMs) to produce training data and assess DDPMs' performance.
result DDPMs can generate contextually correct images, interpolating between samples.
Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to data have been popular in applications such as image denoising, inpainting, and medical image reconstruction. In this work, we focus in…
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synth…
Deep learning model improves medical diagnosis and reduces patient time.
problem Ineffective and slow methods for synthesizing multimodal medical data.
method Cross-modal deep learning architecture with co-attention mechanism.
result Model outperforms previous methods by 2.35% and is 53% more efficient.
Framework synthesizes geological images minimizing patch distribution discrepancy.
problem Synthesizing realistic geological images from a single exemplar.
method Uses kernel discrepancies and generative neural networks for efficient synthesis.
result Synthesized images match visual patterns and spatial statistics of the exemplar.
New method evaluates text-to-image synthesis for realism, variety, and semantic accuracy.
problem Lack of metrics revealing semantic accuracy in text-to-image synthesis.
method Uses Inception network representations and t-SNE visualization for semantic evaluation.
result Classification accuracy of generated images to real images' visual concepts correlates with semantic accuracy.
Study finds little progress in medical machine learning benchmarks over 3 years.
problem Lack of meaningful progress in medical machine learning benchmarks for structured healthcare data.
method Comprehensive review and meta-analysis of benchmarks in medical machine learning for structured data.
result Deep recurrent models perform only better than logistic regression on certain clinical prediction tasks.
Diffusion models outperform GANs in image synthesis quality.
problem Improving image synthesis quality using generative models.
method Developed and optimized diffusion models, and introduced classifier guidance for conditional synthesis.
result Achieved superior image synthesis quality with diffusion models, matching or exceeding GANs in FID scores.
Generative model creates realistic scenes from pixel-wise labels.
problem Creating photo-realistic scenes from pixel-wise labels.
method Semantic bottleneck GAN model combining conditional and unconditional generation networks.
result Model outperforms state-of-the-art models in unsupervised image synthesis.
GANs improve image generation from text and other images.
problem Improving image generation from text and other images.
method Adversarial training to estimate real data distribution and generate synthetic data.
result GANs outperform conventional methods in image generation.
Method treats pseudo healthy synthesis as a factor decomposition problem.
problem Creating a healthy-looking image from a pathological one.
method Adversarial training with paired or unpaired settings, combining two factors (healthy and disease) to reconstruct the input.
result Method outperforms conditional GAN and CycleGAN in generating pseudo healthy images.
A single robust classifier handles complex image synthesis tasks.
problem Challenging image synthesis tasks.
method Adversarial robustness training of a single classifier.
result Direct manipulation of salient features in input images.
A GMM-based method generates new 3D structures from medical images.
problem Generating new medical images from limited data and different modalities.
method Gaussian Mixture Model (GMM) for point-cloud generation.
result Generated point-clouds closely match training samples from the same class.
Deep learning methods quantify uncertainty in neuroimage enhancement.
problem Uncertainty in deep learning models for medical image enhancement.
method Heteroscedastic noise model and approximate Bayesian inference for uncertainty quantification.
result Uncertainty quantification improves predictive performance and risk assessment.
Strong baseline for medical imaging domain adaptation.
problem Improving generalization in medical imaging across different datasets.
method Training on diverse chest X-ray datasets.
result Empirical demonstration of model generalization to out-of-sample domains.
Single-stage neural architecture improves text-to-image synthesis.
problem Combining text and image generation is challenging.
method Used deep residual networks and sentence interpolation.
result Achieved state-of-the-art performance with single-stage training.
Generative model creates meal images from ingredient descriptions.
problem Synthesize photo-realistic meal images from ingredient descriptions.
method Attention-based ingredients-image association model, cycle-consistent constraint.
result Model generates meal images corresponding to ingredient descriptions.
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector …
IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.
problem Reconstructing high-fidelity medical images from incomplete data.
method Image-adaptive GAN-based reconstruction method (IAGAN).
result IAGAN can recover fine structures relevant for medical diagnosis.
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r…
MetalGAN synthesizes images across multiple domains without labels.
problem Synthesizing images across multiple domains using a single network.
method Combines cGAN for image generation and Meta-Learning for domain switch.
result MetalGAN successfully produces multi-domain images without hard-coded labels.
BigGAN achieves state-of-the-art image synthesis on ImageNet.
problem Generating high-fidelity images from complex datasets like ImageNet.
method Trained a large-scale GAN with orthogonal regularization and a truncation trick.
result Improved Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4.
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in…
DreamFusion uses text-to-image diffusion models to create 3D images efficiently.
problem Lack of large-scale 3D datasets and efficient architectures for 3D synthesis.
method Adapting a 2D diffusion model to 3D synthesis using a loss based on probability density distillation.
result A 3D model can be optimized from a 2D diffusion model, allowing for text-to-3D synthesis.
Improved image synthesis with user scribbles and text prompts.
problem Inadequate details in generated images due to domain shift.
method Optimization problem formulation and cross-attention for control.
result Significant improvement in user satisfaction (85.32% higher).
Worldsheet wraps a 3D mesh sheet onto a single image to synthesize novel views.
problem Synthesizing novel views from a single image with large viewpoint changes.
method Shrink-wrapping a planar mesh sheet onto the input image, consistent with learned depth.
result Worldsheet consistently outperforms prior methods on single-image view synthesis.
GDGAN improves medical image accuracy for rare diseases.
problem Poor accuracy for infrequent medical image classes.
method Serial GANs for general and detailed labels.
result GDGAN outperformed existing methods in AUC.
This paper learns hierarchical compositional models for image synthesis.
problem Creating interpretable and hierarchical image representations.
method Sparsifying a generator network to induce an AND-OR hierarchy.
result The method learns meaningful and interpretable hierarchical representations.
Review of deep learning methods in medical image registration.
problem Improving accuracy and efficiency of medical image registration.
method Classification and detailed analysis of seven categories of DL-based registration methods.
result Comprehensive comparison of DL-based methods for lung and brain registration.
Deep learning improves MRI analysis, promising future in medical imaging.
problem Improving accuracy and efficiency in MRI analysis.
method Application of deep artificial neural networks to MRI processing.
result Deep learning outperforms traditional methods in MRI analysis.
Generative models learn complex spatial patterns using program synthesis.
problem Capturing complex global structure in data, especially in images.
method Incorporates programs representing global structure into generative models and learns these models through program synthesis.
result Significantly better at generating and completing images with global structure compared to state-of-the-art methods.
Program synthesis struggles with complex spatial relationships in image classification.
problem Challenges in solving Synthetic Visual Reasoning Test problems.
method Quantitative reanalysis of human and machine performance, improved program synthesis classifier, categorization of SVRT problems.
result Program synthesis is constrained by spatial relationships in images, not just shape specification.
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
problem Medical image denoising often results in loss of fine structures.
method Conditional diffusion model with stabilized reverse sampling and supervised training.
result DiffDenoise outperforms state-of-the-art methods in medical image denoising.
Machine vision-guided 3D medical image compression improves segmentation accuracy.
problem High data traffic and computation costs in cloud-based medical image analysis.
method Developed a machine vision-oriented 3D image compression framework for medical segmentation.
result Significantly higher segmentation accuracy at the same compression rate or better compression rate under the same accuracy.
Transfer learning offers little benefit in medical imaging tasks.
problem Understanding transfer learning's impact on medical imaging tasks.
method Evaluation of transfer learning on two large-scale medical imaging tasks.
result Simple, lightweight models perform comparably to ImageNet architectures.
Unified deep learning predicts Parkinson's disease from medical images.
problem Diagnosing Parkinson's disease accurately from medical images.
method Transfer learning and domain adaptation using deep convolutional and recurrent neural networks.
result The approach effectively predicts Parkinson's disease across different medical environments.
A GAN variant synthesizes missing MRI sequences from available ones.
problem Missing MRI sequences due to various constraints.
method Multi-modal Generative Adversarial Network (GAN) that combines multiple available sequences to synthesize missing ones.
result The proposed GAN method outperforms competing approaches in synthesizing missing MRI sequences.
A new algorithm improves medical image grading accuracy.
problem Improving automatic grading of medical images for disease severity or risk score.
method Sparse range-constrained learning (SRCL) algorithm integrating sparse representation and grading.
result Improves accuracy in cup-to-disc ratio computation and cataract grading.
A new NAS framework optimizes 3D medical image segmentation architectures.
problem Optimizing neural architectures for high-resolution 3D medical images.
method Stochastic sampling algorithm for scalable gradient-based optimization of neural connectivities and operation types in both encoder and decoder.
result Automatically designed architecture outperforms human-designed U-Net.