Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network
Bibliographic record
Abstract
Ultrasound images are often affected by limited resolution, artifacts, and inherent speckle noise. To address these challenges, researchers have explored denoising approaches. Recently, deep learning methods have demonstrated distinct advantages in ultrasound image denoising. However, further improvements are needed to preserve structural details, such as boundaries, edges, and margins. This paper proposes a hybrid CNN-transformer network called HCTSpeckle, an encoder-decoder network with a fusion block designed to enhance ultrasound images. The fusion block combines swin transformers to capture global modeling relationships, and convolutional neural networks to extract local modeling details. It is integrated into the encoder-decoder structure, allowing the model to focus on both local and global texture structural information. An improved swin block is also introduced into the network to improve robustness by extracting more significant features. HCTSpeckle was evaluated both quantitatively and qualitatively with clinical objectives using two public and two private datasets. Both results showed that HCTSpeckle significantly enhanced the ultrasound image quality and outperformed state-of-the-art methods in noise reduction and structure preservation across all four datasets. Compared to existing denoising methods, HCTSpeckle achieved notably faster performance in terms of complexity comparison, such as parameter counts, gigaFLOPs, and inference time. Moreover, this study assessed the effectiveness of HCTSpeckle for alveolar bone segmentation using dental images, demonstrating that HCTSpeckle significantly improved segmentation performance. Furthermore, an experienced radiologist blindly rated the 250 dental US images on a scale of 1 to 5, with 5 being the highest image quality, showing that HCTSpeckle consistently produced higher-quality images.
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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) | 0.001 | 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".