Optimizing U-Net to Segment Left Ventricle from Magnetic Resonance Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Left ventricle segmentation is an important medical imaging task to measure several diagnostic parameters related to the heart such as ejection fraction and stroke volume. Recently, convolutional neural networks (CNN) have shown great potential in achieving state-of-the-art segmentation results for such applications. However, most of the existing research is focusing on building complicated variations of the neural networks with modest changes to their performance. In this study, the popular U-Net architecture is optimized by analyzing its behaviour once fully trained from which one can simplify its architecture by fixing layers weights or eliminating some of them completely. For instance, by performing a Fourier analysis of the convolution at each layer, we were able to discover that some early layers can be approximated by simple uniform filters. Furthermore, in a separate experiment by removing the middle layers of the U-Net one can reduce the number of U-Net parameters from 31 million to 0.5 million weights without compromising its performance. The experimental evaluations show that the new optimized U-Net achieves 0.93 for the Dice score in comparison to manual ground truth.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it