Bibliographic record
Abstract
Radiology is a critical tool for physicians when treating many health complications. X-ray computed tomography (X-ray CT) is particularly useful in highlighting lesions and damaged tissue in the body. The adoption of X-ray tomography is seeing significant growth as the technology improves and radiologists are finding it increasingly useful. A significant drawback of X-ray CT is the associated radiation exposure which is harmful to the body. If the radiation dose is decreased in the acquisition protocol, the images become degraded and no longer useful for radiologists. Current methods for reducing radiation risk include guidelines on acceptable exposure, and very recently reconstruction techniques which aim to denoise images acquired at low radiation dose. However, image post-processing techniques are preferred because they are scanner independent, and don’t require the denoising algorithm to have access to the raw scanner data which is often not made available by manufacturers. With advances in deep-learning via convolutional neural networks, several methods have been proposed to denoise low dose CT images in order to predict the normal dose image. In this work, existing methods are improved on with the addition of our contributions. We propose a training regime using a cascade of neural networks the first of which uses a perceptual loss, and the second which performs a residue prediction using mean-squared-error. Secondly, we propose a new loss function which incorporates perceptual loss, structural dissimilarity and mean-squared-error. We show that both proposed methods result in significant improvement when compared to the related works. As a secondary contribution, this work recommends several design considerations for building deep learning networks for image denoising. We show results from extensive empirical study to support our recommendations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".