Optimization of endometrial tolerance ultrasound image reconstruction algorithm supported by machine vision technology
Bibliographic record
Abstract
In today's society, hospitals are treated with images generated by medical examination equipment for disease diagnosis, and high-resolution images can greatly improve the accuracy of doctors' disease diagnosis.The study constructs an ultrasound image dataset US-Dataset suitable for the task of superresolution reconstruction of ultrasound images.Based on this ultrasound image dataset, a degradation model is proposed, which in turn constructs ultrasound image matching pairs containing high -low resolution images for training the model proposed in this paper.To improve the perceptual quality of endometrial images, a super-resolution reconstruction model UN-SRGAN based on generative adversarial network is proposed in this paper.The network structure of this model consists of a generator and a discriminator.To validate the effectiveness of the model proposed in this paper, it is evaluated on Accuracy, Precision, Recall, Specificity, and F1-score metrics.The proposed model achieves the lead on PSNR and SSIM metrics and subjective quality evaluation, and the UN-SRGAN model has an accuracy of 0.9721, which is better than the other models, verifying the effectiveness of the model.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".