Swin-EchoNet: Deep Learning-based Two-Chamber Segmentation of 2D Echocardiography using Swin Transformer
Bibliographic record
Abstract
Abstract The initial crucial stage in recognizing heart-related problems involves making a precise diagnosis. Live heart images can be obtained through techniques such as MRI, CT scan, and Echocardiography. Determining significant cardiac parameters for disease diagnosis, such as systolic and diastolic volumes, ejection fraction, and left atrium (LA), requires accurate segmentation of the left ventricle in echocardiography images. However, automated segmentation of these images is a complex and challenging task. Therefore, there is a requirement for a fully automatic method that can accurately segment cardiac images and save time. We utilized our proposed model Swin-echonet architecture for accurate left ventricle segmentation in echocardio-graphy images. Our method has been successfully tested on two separate datasets, namely the CAMUS dataset with 1800 echocardiographic images and data from a hospital with 1550 echo images. On the CAMUS dataset, we achieved a mean dice coefficient of 0.951±0.2271. Additionally, our method produced a mean IoU of 0.7263 and a mean Dice coefficient of 0.9738 on the second dataset. The obtained results demonstrate the efficiency of our method across diverse datasets, indicating its potential to assist medical professionals in the detection and treatment of cardiac problems.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".