Medical Image Generation
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Benchmarks
Most implemented
NiftyNet: a deep-learning platform for medical imaging
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
Skin Lesion Synthesis with Generative Adversarial Networks
Papers
Compositional Reward Models for Conditional Medical Image Generation
Acquiring high quality annotated medical image data is critical for training deep learning models; however, annotation is expensive, time consuming, and requires domain expertise. Conditional diffusion models, such as Co…
Skin Lesion ClassificationMedical Image GenerationReinforcement LearningCell SegmentationWhen the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imaged subject are altered, while keeping their identity fixed. Most exi…
Medical Image GenerationImage EditingParameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering
The major limitations of gastrointestinal (GI) endoscopy AI systems arise from a shortage of annotated data, strict privacy policies, and significant bottlenecks in conventional model fine-tuning. Such limitations impede…
Synthetic Data GenerationVisual Question AnsweringMedical Image GenerationLiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators
High-resolution 3D medical image generation remains challenging because fully volumetric models are computationally expensive, while efficient 2D slice generators often fail to preserve anatomical consistency across the …
Medical Image GenerationGenerative Drifting for Conditional Medical Image Generation
Conditional medical image generation plays an important role in many clinically relevant imaging tasks. However, existing methods still face a fundamental challenge in balancing inference efficiency, patient-specific fid…
Medical Image GenerationVolDiT: Controllable Volumetric Medical Image Synthesis with Diffusion Transformers
Diffusion models have become a leading approach for high-fidelity medical image synthesis. However, most existing methods for 3D medical image generation rely on convolutional U-Net backbones within latent diffusion fram…
Medical Image Generation