Region of Interest Image Coding for Digital Mammography
Bibliographic record
Abstract
In this paper, we investigate region-based wavelet compression methods and describe a region-based coder based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm, called unbalanced spatial orientation trees (UBT), applied to digital mammograms. We compare this method against the region-based extension of SPIHT (ROI-SPIHT), and the ROI coding unit of JPEG2000 (JP2K) algorithm on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bits per pixel (bpp). We show that UBT is competitive in PSNR with the other two region-based coding methods, also providing a more general multiregion multiquality coding framework rather than ROI/non-ROI coding. Unlike ROI-SPIHT, UBT allows encoding of diagnostically significant regions with best possible fidelity while allocating less number of bits to remaining regions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".