UNeXt: a Low-Dose CT denoising UNet model with the modified ConvNeXt block
Bibliographic record
Abstract
In recent decades, clinicians have widely utilized computed tomography (CT) for medical diagnosis. Medical radiation is potentially hazardous and therefore reducing x-ray radiation in CT scanning is desired. However, decreasing radiation dose leads to increased noise and artifacts. In this paper, low-dose CT images (LDCT) have been denoised in the UNet-based novel architecture of convolutional neural network (CNN) and compared with normal-dose images (NDCT). A multi-feature extraction block (MFEB) is placed to get extra features in the different receptive fields. The modified ConvNeXt block for CT images (CTNeXt) is developed to extract diverse feature data at various scales. Furthermore, we introduced the image reconstruction block to gradually merge the group convolutions’ feature information and eliminate the gap between the features to ease the transmission of multi-scale information from subsequent stages. The network is optimized using the integration of mean-squared error (MSE), mean-absolute error (MAE), and contrastive loss via vgg16-net. These functions show that they could effectively prevent edge over-smoothing, improve image texture, and preserve structural details. A comparative analysis of the proposed network demonstrates that our method outperforms state-of-the-art denoising models, such as Wasserstein Generative Adversarial Network (WGAN-vgg) and Residual Convolutional Encoder-Decoder (RED-CNN).
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".