Stable Diffusion Model-Based Scintigraphy Image Synthesis: Data Augmentation Toward Enhanced Multiclass Thyroid Diagnosis
Bibliographic record
Abstract
The objective of this study is to assess the efficacy of advanced augmentation techniques, such as stable diffusion, in improving the performance of deep learning models in the classification of scintigraphic thyroid images. In this retrospective study, 2983 anterior view scintigraphic images were collected and subsequently categorized into four thyroid conditions. Both stable diffusion and conventional augmentation techniques were utilized. The generated images, alongside real images, were used to train a ResNet101V2 architecture under six different training strategies. The strategies were assessed against external datasets to evaluate model performance in terms of accuracy, precision, recall, and F1-score. The use of synthetic data in training led to consistently superior performances compared to training with only real data. Specifically, the models trained with synthetic data augmentation demonstrated higher precision and recall. The incorporation of synthetic images generated via stable diffusion significantly enhanced the diagnostic capabilities of AI models in thyroid scintigraphy interpretation. This approach not only improves the classification accuracy but also provides a viable solution to the challenge of data scarcity in medical imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".