Mammogram Image Superresolution Based on Statistical Moment Analysis
Why this work is in the frame
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Bibliographic record
Abstract
A novel super resolution method for enhancing the resolution of mammogram images based on statistical moment analysis (SMA) has been designed and implemented. The proposed SMA method enables high resolution mammogram images to be produced at lower levels of radiation exposure to the patient. The SMA method takes advantage of the statistical characteristics of the underlying breast tissues being imaged to produce high resolution mammogram images with enhanced fine tissue details such that the presence of masses and micro calcifications can be more easily identified. In the SMA method, the super resolution problem is formulated as a constrained optimization problem using an adaptive third-order Markov prior model, and solved efficiently using a conjugate gradient approach. The priors are adapted based on the inter-pixel likelihoods of the first moment about zero (mean), second central moment (variance), and third and fourth standardized moments (skewness and kurtosis) from the low resolution images. Experimental results demonstrate the effectiveness of the SMA method at enhancing fine tissue details when compared to existing resolution enhancement methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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