Adaptive windowing for optimal visualization of medical images based on normalized information distance
Why this work is in the frame
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Bibliographic record
Abstract
There has been a growing recent interest of applying Kol-mogorov complexity and its related normalized information distance (NID) measures in real-world problems, but their application in the field of medical image processing remains limited. In this work we attempt to incorporate NID in the design of windowing operators for optimal visualization of high dynamic range (HDR) medical images, where predefined intensity interval of interest needs to be mapped to match the low dynamic range (LDR) of standard displays. By approximating NID using a Shannon entropy based method, we are able to optimize parametric windowing operators to maximize the information similarity between the HDR image and the LDR image after mapping. Experimental results demonstrate promising performance of the proposed approach.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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