Radiomics-Driven Diffusion Model and Monte Carlo Compression Sampling for Reliable Medical Image Synthesis
Bibliographic record
Abstract
Reliable medical image synthesis is crucial for clinical applications and downstream tasks, where high-quality anatomical structure and predictive confidence are essential. Existing studies have made significant progress by embedding prior conditional knowledge, such as conditional images or textual information, to synthesize natural images. However, medical image synthesis remains a challenging task due to: 1) Data scarcity: High-quality medical text prompt are extremely rare and require specialized expertise. 2) Insufficient uncertainty estimation: The uncertainty estimation is critical for evaluating the confidence of reliable medical image synthesis. This paper presents a novel approach for medical image synthesis, driven by radiomics prompts and combined with Monte Carlo Compression Sampling (MCCS) to ensure reliability. For the first time, our method leverages clinically focused radiomics prompts to condition the generation process, guiding the model to produce reliable medical images. Furthermore, the innovative MCCS algorithm employs Monte Carlo methods to randomly select and compress sampling steps within the denoising diffusion implicit models (DDIM), enabling efficient uncertainty quantification. Additionally, we introduce a MambaTrans architecture to model long-range dependencies in medical images and embed prior conditions (e.g., radiomics prompts). Extensive experiments on benchmark medical imaging datasets demonstrate that our approach significantly improves image quality and reliability, outperforming SoTA methods in both qualitative and quantitative evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".