Fixed block-based lossless compression of digital mammograms
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
Breast cancer is a leading cause of death among women in Canada. Computer-aided diagnosis of mammograms (X-ray films of breast tissue) is a noninvasive and an inexpensive way of diagnosing breast cancer. The objective of this project is to investigate image compression schemes for faithful transmission and reproduction of digital mammography data over a communication link. A fixed block-based (FBB) near lossless compression scheme for mammograms has been developed which runs in conjunction with traditional compression schemes such as Huffman coding and Lempel-Ziv Welch (1978) coding. The algorithm codes blocks of pixels within the image that contain the same intensity value (the odds of having blocks of the same pixel values in a mammography image are very high), thus reducing the size of the image substantially while encoding the image at the same time. The proposed compression scheme was applied on 44 mammograms (22 benign and 22 malignant), and the compression scheme provided a compression ratio of 1.7:1. When Huffman (1952) coding and LZW coding were used in conjunction with the FBB compression scheme, the compression ratio was 3.81:1 for Huffman, and 5:1 for LZW coding. The proposed FBB lossless compression technique seems to be promising for teleradiology applications.
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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 | 0.001 | 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