Identification of Acute Myocardial Infarction from Left Ventricular Wall Rupture Using ResNet 18-Deep Active Learning Algorithms
Bibliographic record
Abstract
Acute myocardial infarction (AMI) is a heart muscle ischemia caused by blockage or narrowing of coronary arteries, leading to left ventricular wall rupture (LVWR).Diagnosing AMI is challenging due to improper border and edge enhancement, segmentation, and classification.To address the above-mentioned, a deep denoised convolutional neural network (DnCNN) is applied to enhance the edge and boundary regions of the myocardium.A ResNet 18-based deep active curriculum learning (DACL) model is proposed to classify MI or non-MI patients by left ventricular wall rupture.The model is trained with a few samples to detect MI and dynamically updates the number of samples in the training dataset.The adaptive sampling strategy efficiently classifies myocardial infarction in the HMC-QU dataset, achieving a sensitivity of 98.2%, a specificity of 97.3%, an accuracy of 98.5%, and an AUC of 99.6%.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".