Multiview 3-D Echocardiography Image Fusion with Mutual Information Neural Estimation
Bibliographic record
Abstract
Multiview three-dimensional echocardiography (M3DE) fuses volumetric datasets acquired from complementary acoustic windows to expand field-of-view and allow for visualization of the entire heart. This is of great importance for cardiac chamber quantification. The M3DE also allows for image quality improvement through fusion of single views on overlapping regions. However, shape variations and increase in noise stemming from the nature of ultrasound physics make fusion a challenging task. This study proposes a novel machine learning-based fusion method to combine ultrasound views that are spatially apart, namely, apical and parasternal. Our method jointly uses: 1) an autoencoder framework to generate the fused image; and 2) a mutual information neural estimation network to maximize the mutual information between source and fused images. The experimental evaluations show promising results and the fused image generated by the proposed method improves the signal-to-noise ratio by up to 18.23 dB and the contrast-to-noise ratio by up to 21.76 dB compared to the state-of-art approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".