ProtoASNet: Comprehensive evaluation and enhanced performance with uncertainty estimation for aortic stenosis classification in echocardiography
Bibliographic record
Abstract
Aortic stenosis (AS) is a prevalent heart valve disease that requires accurate and timely diagnosis for effective treatment. Current methods for automated AS severity classification rely on black-box deep learning techniques, which suffer from a low level of trustworthiness and hinder clinical adoption. To tackle this challenge, we propose ProtoASNet, a prototype-based neural network designed to classify the severity of AS from B-mode echocardiography videos. ProtoASNet bases its predictions exclusively on the similarity scores between the input and a set of learned spatio-temporal prototypes, ensuring inherent interpretability. Users can directly visualize the similarity between the input and each prototype, as well as the weighted sum of similarities. This approach provides clinically relevant evidence for each prediction, as the prototypes typically highlight markers such as calcification and restricted movement of aortic valve leaflets. Moreover, ProtoASNet utilizes abstention loss to estimate aleatoric uncertainty by defining a set of prototypes that capture ambiguity and insufficient information in the observed data. This feature augments prototype-based models with the ability to explain when they may fail. We evaluate ProtoASNet on a private dataset and the publicly available TMED-2 dataset. It surpasses existing state-of-the-art methods, achieving a balanced accuracy of 80.0% on our private dataset and 79.7% on the TMED-2 dataset, respectively. By discarding cases flagged as uncertain, ProtoASNet achieves an improved balanced accuracy of 82.4% on our private dataset. Furthermore, by offering interpretability and an uncertainty measure for each prediction, ProtoASNet improves transparency and facilitates the interactive usage of deep networks in aiding clinical decision-making. Our source code is available at: https://github.com/hooman007/ProtoASNet.
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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 | 0.001 |
| 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".