DiffusionClusNet: Deep Clustering-Driven Diffusion Models for Ultrasound Image Enhancement
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Bibliographic record
Abstract
In modern medical diagnostics, high-quality ultrasound images are essential because they are cost-effective, non-invasive, and capable of providing dynamic recordings. Nevertheless, obtaining such high-quality images is challenging, especially in resource-limited areas, which negatively impacts diagnostic accuracy. To address these issues, we propose a novel method for enhancing ultrasound images using deep clustering-enhanced diffusion models. Our proposed method consists of two main components: an image enhancement pathway and an Auxiliary Classification Pathway (ACP), which are integrated through a Fusion of Image and Classification (FIC) module. The image enhancement pathway employs a structure that includes a Variational Autoencoder (VAE) encoder, a UNet denoising network, and a VAE decoder. This structure progressively reduces noise and generates high-quality images. Simultaneously, the ACP utilizes a convolutional neural network, a transformer encoder, and a clustering module to extract classification information, which supports the enhancement process. The FIC module uses a cross-attention mechanism to merge the image and classification features, thus enhancing the overall performance of image enhancement. To ensure the generated images retain their structural integrity, Structural Similarity (SSIM) loss is employed. Experiments conducted on multiple ultrasound datasets reveal that our method surpasses existing techniques in terms of peak signal-to-noise ratio and SSIM scores. Clinically, our approach significantly improves image contrast and structural detail, leading to more accurate diagnoses. This diffusion-based strategy for image enhancement and classification feature fusion introduces a fresh perspective on preserving structure and enhancing detail in medical image processing. Our Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ichbincn/Ultrasound-Enhancement</uri>.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it