Multi-Scale Dilation with Residual Fused Attention Network for Low Dose CT Noise Artifact Reductions
Bibliographic record
Abstract
Computed Tomography (CT) Scans produce more than half the radiation exposure from medical use which results in problems for long term use of these expensive machines. Some solutions have involved reducing the radiation dose, however that leads to noise artifacts making the low-dose CT (LDCT) images unreliable for diagnosis. In this study, a Multi-scale Dilation with Residual Fused Attention (MD-RFA) deep neural network is proposed, more specifically a network with an integration with a multi-scale feature mapping, spatial- and channel-attention module to enhance the quality of LDCT images. Further, the multi-scale image mapping uses a series of dilated convolution layers, which promotes the model to capture hierarchy features of different scales. The attention modules are combined in a parallel connection and are described as a Boosting Attention Fusion Block (BAFB) that are then stacked on top of one another creating a residual connection known as a Boosting Module Group (BMG).
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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 | 0.001 |
| 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 | 0.000 | 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".