X-hance an interface that enhances x-ray images
Bibliographic record
Abstract
To aid medical professionals in their timely and accurate diagnoses of patients, powerful and accurate diagnostic tools are required. Image processing software tools improve the quality and accuracy of x-ray images used in medical diagnoses. The scope of this project encompasses the design and use of X-hance, an interface created to enhance image quality issues of various medical images which are taken from the MedPix R-CME Cases Database. These images have some problems, such as blurriness, undefined or unclear edges, unfair distribution of contrast between bone and tissue structure, darkness, and bad tonal rendering. Our main purpose is to process these problematic images using different functionalities of X-hance. These functionalities are one push button to upload the unprocessed image, another push button to preprocess the image, and three sliders that can be used for adjusting signal equalization, tonal rendering, and display compensation. The advantage of using sliders in an interface is enabling the user to correct images by manually controlling the parameters of the algorithm when moving the sliders left or right and stopping at the desired position to obtain an improved quality of the x-ray image.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.008 |
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".