Deep Learning for Low-Dose CT Noise Removal Using Dilated Convolution and Perceptual Loss
Bibliographic record
Abstract
Low-dose computed tomography has been recommended to reduce the radiation risks of CT scans for patients. However, the reconstructed CT image will be considerably degraded because of photon starvation. Both traditional noise removal techniques and neural networks have been used to enhance the quality of low-dose CT images. In this study, a deep neural network is proposed to mitigate this problem. The network employs dilated convolution, batch normalization, and residual learning. Moreover, a nontrainable edge detection layer is proposed helping to produce sharper edges in the output image without introducing additional complexity. This network is optimized by a combination of mean-square error and perceptual loss to preserve textural details in the CT image that are critical for diagnosis. This objective function solves the over-smoothing problem and grid-like artifacts caused by per-pixel loss and perceptual loss, respectively. The experiments demonstrate the effects of each modification to the network and confirm that the proposed network achieves better performance relative to the state of the art methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".