Accurate estimation of density and background parenchymal enhancement in breast MRI using deep regression and transformers
Bibliographic record
Abstract
Early detection of breast cancer is important for improving survival rates. Based on accurate and tissue-specific risk factors, such as breast density and background parenchymal enhancement (BPE), risk-stratified screening can help identify high-risk women and provide personalized screening plans, ultimately leading to better outcomes. Measurements of density and BPE are carried out through image segmentation, but volumetric measurements may not capture the qualitative scale of these tissue-specific risk factors. This study aimed to create deep regression models that estimate the interval scale underlying the BI-RADS density and BPE categories. These models incorporate a 3D convolutional encoder and transformer layers to comprehend time-sequential data in DCE-MRI. The correlation between the models and the BI-RADS categories was evaluated with Spearman coefficients. Using 1024 patients with a BI-RADS assessment score of 3 or less and no prior history of breast cancer, the models were trained on 50% of the data and tested on 50%. The density and BPE ground truth labels were extracted from the radiology reports using BI-RADS BERT. The ordinal classes were then translated to a continuous interval scale using a linear link function. The density regression model is strongly correlated to the BI-RADS category with a correlation of 0.77, slightly lower than segmentation %FGT. The BPE regression model with transformer layers shows a moderate correlation with radiologists at 0.52, similar to the segmentation %BPE. The deep regression transformer has an advantage over segmentation as it doesn’t need time-point image registration, making it easier to use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".