Conditional Diffusion Model for X-Ray Segmentation Data Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays training a well-functioning deep learning AI model requires a large amount of data, while in the field of medicine many scenarios lack training data due to privacy issues and legal reasons. In this essay, we propose to use ControlNet, a novel approach that leverages stable diffusion models and conditional control to produce realistic and diverse medical images. ControlNet allows us to specify extra conditions that the diffusion model should follow, such as edge maps, depth maps, segmentation masks, or CLIP image embeddings. These conditions can help us to preserve the structure, shape, and semantics of the target organs or tissues, as well as to manipulate the appearance, style, and context of the generated images. Specifically, we will use ControlNet to generate X-ray of a patient with pulmonary nodules and show the improvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it