Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions
Bibliographic record
Abstract
X-ray computed tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose CT (LDCT) image reconstruction techniques have offered a new research area. In this study, a deep neural network is proposed, specifically a residual network (ResNet) using dilated convolution, batch normalization, and rectified linear unit (ReLU) layers with fused spatial- and channel-attention modules to enhance the quality of LDCT images. The network is optimized using the integration of per-pixel loss, perceptual loss via VGG16-net, and dissimilarity index loss. Through an ablation experiment, these functions show that they could effectively prevent edge oversmoothing, improve image texture, and preserve the structural details. Finally, comparative experiments showed that the qualitative and quantitative results of the proposed network outperform state-of-the-art denoising models such as block-matching 3D filtering (BM3D), Markovian-based patch generative adversarial network (patch-GAN), and dilated residual network with edge detection (DRL-E-MP).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".