Generation of 3D MRI-Intensity Breast Images Using a Class Conditional Latent Diffusion Model
Bibliographic record
Abstract
The purpose of this research is to create a machine-learning model that generates high quality 3D MRI-intensity images of the breast based on user defined input of desired features (i.e., BI-Rads class, tumours, etc.). In this particular study, we develop a technique that uses a conditional 3D latent diffusion model. The training set consists of 94 high-resolution 3D MRI intensity images obtained from a public-domain database. During training, computational costs are reduced by using the latent space of a separately trained autoencoder that compresses the images. Thus, the diffusion model generates images in the latent space, which are subsequently converted to high resolution images using the decoder of the trained autoencoder. Current conditioning is based on a modified version of BI-Rads density classification. It is found that the latent space preserves key structural and textural features of the original MRI data, enabling consistent reconstruction and effective class separation. The generated 3D images exhibit high visual fidelity and maintain continuity across slices, indicating that the model has successfully captured the underlying data distribution. Having 3D MRI intensity images will allow us to generate labeled complex-valued permittivity phantoms for use in microwave imaging research.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".