GEDFormer: Gradient Edge Detection in LDCT Image Denoising Transformer Model
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have excelled in deep learning applications, including medical image processing, due to their ability to extract hierarchical feature representations. While CNNs excel at capturing local features, their fixed receptive fields can make it challenging to effectively model global contextual information, essential for certain tasks, such as medical image denoising. To address this, Vision Transformers (ViTs) have emerged as an alternative, leveraging self-attention mechanisms to capture both global and local dependencies within images. This study explores the use of a standalone ViT-based framework for denoising low-dose computed tomography (LDCT) images with a self-guided gradient edge detecting attention module, which aims to preserve critical spatial and frequency details required for accurate diagnostic outcomes. The proposed method is rigorously evaluated by comparing its performance against state-of-the-art traditional CNN models (BM3D, DSC-GAN, RED-CNN) and a hybrid CNN-ViT model (TED-Net). Both numerical data analysis and image inspection are used to demonstrate the efficacy of the ViT-based approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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 | 0.001 |
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".