Papers Medical Image Generation
“Medical Image Generation” 태그가 달린 논문 97편 · 필터 해제
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 GenerationCompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation
Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that generators produce equally high-quality images across demographic groups. Models…
Medical Image GenerationVisually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement
Medical image synthesis is crucial for alleviating data scarcity and privacy constraints. However, fine-tuning general text-to-image (T2I) models remains challenging, mainly due to the significant modality gap between co…
Medical Image GenerationOptimizing 3D Diffusion Models for Medical Imaging via Multi-Scale Reward Learning
Diffusion models have emerged as powerful tools for 3D medical image generation, yet bridging the gap between standard training objectives and clinical relevance remains a challenge. This paper presents a method to enhan…
Medical Image GenerationReinforcement LearningAn Interpretable Local Editing Model for Counterfactual Medical Image Generation
Counterfactual medical image generation have emerged as a critical tool for enhancing AI-driven systems in medical domain by answering "what-if" questions. However, existing approaches face two fundamental limitations: F…
Medical Image GenerationMedVAR: Towards Scalable and Efficient Medical Image Generation via Next-scale Autoregressive Prediction
Medical image generation is pivotal in applications like data augmentation for low-resource clinical tasks and privacy-preserving data sharing. However, developing a scalable generative backbone for medical imaging requi…
Medical Image GenerationData AugmentationA Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Models
Image generative models are known to duplicate images from the training data as part of their outputs, which can lead to privacy concerns when used for medical image generation. We propose a calibrated per-sample metric …
Medical Image GenerationA Fast and Efficient Modern BERT based Text-Conditioned Diffusion Model for Medical Image Segmentation
In recent times, denoising diffusion probabilistic models (DPMs) have proven effective for medical image generation and denoising, and as representation learners for downstream segmentation. However, segmentation perform…
Medical Image SegmentationMedical Image GenerationClinical KnowledgeBeyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets
Medical image datasets frequently exhibit significant class imbalance, a challenge that is further amplified by the inherently limited sample sizes that characterize clinical imaging data. Using human embryo time-lapse i…
Medical Image GenerationCycle Diffusion Model for Counterfactual Image Generation
Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or counterfactual generation remains a chal…
Medical Image GenerationData AugmentationImagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance
Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of these models is largely enabled by extensive, readily available pretrained founda…
Medical Image GenerationAdaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis
Medical image annotation is constrained by privacy concerns and labor-intensive labeling, significantly limiting the performance and generalization of segmentation models. While mask-controllable diffusion models excel i…
Medical Image GenerationPixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts. Traditional generative architectures such as Generative Adversarial Networks (G…
Data AugmentationImage GenerationMedical Image GenerationMedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solution by generating synthetic images, thei…
Medical Image SegmentationMedical Image GenerationComputational EfficiencyData AugmentationTRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods suffer from limited anatomical fidelity…
Medical Image GenerationComputational EfficiencyData Augmentation