Medical image compression based on region of interest using better portable graphics (BPG)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Everyday, an enormous number of medical images are produced by hospitals and medical imaging center for research, surgical and disease diagnostics. Therefore, compression is necessary for storing, managing and transferring these data to make storage manageable. Medical images have some parts which are more important called region of interest (ROI) with useful information for the diagnostic purpose that should be reconstructed with high quality during the image decompression process. In this paper, a state-of-the-art image compression format known as Better Portable Graphics (BPG), which is based on the High Efficiency Video Coding (HEVC), is used for medical image compression. In the proposed compression method, first the medical image is segmented into two parts: ROI and non-ROI regions. In the next step, lossless BPG compression algorithm is applied to the ROI areas, and lossy BPG is utilized for non-ROI regions. In the end, the resulting reconstructed images are combined to create a complete compressed image. The MRI scan dataset hosted by the University of Cyprus is used to evaluate the performance of the proposed compression method to demonstrate improvement between 10-25% in the compression rate compared to traditional image compression techniques used in the medical industry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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