Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose\n CT Denoising
Bibliographic record
Abstract
Low Dose CT Denoising research aims to reduce the risks of radiation exposure\nto patients. Recently researchers have used deep learning to denoise low dose\nCT images with promising results. However, approaches that use\nmean-squared-error (MSE) tend to over smooth the image resulting in loss of\nfine structural details in low contrast regions of the image. These regions are\noften crucial for diagnosis and must be preserved in order for Low dose CT to\nbe used effectively in practice. In this work we use a cascade of two neural\nnetworks, the first of which aims to reconstruct normal dose CT from low dose\nCT by minimizing perceptual loss, and the second which predicts the difference\nbetween the ground truth and prediction from the perceptual loss network. We\nshow that our method outperforms related works and more effectively\nreconstructs fine structural details in low contrast regions of the image.\n
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".