Direct aneurysm volume estimation by multi-view semi-supervised manifold learning
Bibliographic record
Abstract
Accurate volume estimation of left atrial aneurysm plays an essential role in the early diagnosis and therapy planning. However, it is a challenging task due to huge shape variabilities of aneurysms and great appearance variations of images, which tends to be intractable for segmentation methods. In this paper, we propose a novel estimation method for direct estimation of atrial aneurysm volumes without segmentation. To handle the high variabilities and variations, we propose a new multi-view semi-supervised manifold learning (MSML) algorithm, which fuses multiple complementary features to generate compact, informative and discriminative aneurysm image representation by leveraging both labeled and unlabeled data. Based on the obtained image representation, we adopt random regression forests to conduct direct volume estimation. Our method for the first time achieves a fully automatic estimation of left atrial aneurysm volumes. Experiments on a clinical dataset of 67 subjects with a total of 1220 images show that our method achieves a high correlation co-efficient of 0.91 with ground truth manually labelled by clinical experts and largely outperforms other methods, which demonstrates the effectiveness for aneurysm volume estimation and indicates its potential use in clinical practise.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".