Fixed block-based lossless compression of digital mammograms
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".