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2026-10-20, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20260266 Question.1
Questioner: mrajhans@paloaltonetworks.com 2026-02-11 오후 12:16:18 |
ICACT20260266 Answer.1
Answer by Auhor kiramatullah541@gmail.com 2026-02-11 오후 12:16:18
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| How well does CardioViT-CAM generalize to echocardiographic data from different hospitals, ultrasound machines, and patient demographics, given that training and evaluation rely on a single-center private dataset? |
That is an important concern. First, the model uses self-supervised Masked Autoencoder pretraining on 74,000 unlabeled echocardiography frames. This large-scale pretraining allows the network to learn fundamental cardiac structural patterns rather than memorizing dataset-specific characteristics. As a result, the learned features are more transferable to different clinical environments.
Second, the framework is based on a Vision Transformer architecture, which captures global anatomical relationships through attention mechanisms. Compared to traditional convolution-based models, transformers typically show improved adaptability to variations in imaging protocols, ultrasound devices, and scanning conditions.
Third, we applied extensive data augmentation, including geometric transformations, contrast variation, and noise simulation. These augmentations mimic real-world variability across hospitals, ultrasound machines, and patient populations, which improves model robustness.
In addition, Grad-CAM++ visualizations confirm that the model focuses on clinically meaningful cardiac defect regions rather than machine-specific artifacts, which further supports its reliability.
Although external multi-center validation is planned as future work, the combination of large-scale self-supervised learning, transformer-based feature extraction, and strong internal performance suggests that CardioViT-CAM has strong potential to generalize across diverse echocardiographic settings.
Currently, multi-center annotated CHD datasets are very limited. Therefore, we focused on large-scale self-supervised learning to compensate for data diversity. We are actively planning multi-institution collaborations for external validation.
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ICACT20260266 Question.2
Questioner: quandh13@fe.edu.vn 2026-02-12 오후 12:33:38 |
ICACT20260266 Answer.2
Answer by Auhor kiramatullah541@gmail.com 2026-02-12 오후 12:33:38
Chrome Click!! |
| Masked Autoencoders (MAE) are a commonly used self-supervised learning technique. However, in the context of your task, it remains unclear how MAE improves the performance of the downstream task. Could you please clarify this point through an ablation study or by comparing against a purely supervised learning pipeline? |
Thank you for the comment. The role of MAE in our framework is to improve feature representation learning using large-scale unlabeled echocardiographic data. In the first stage, MAE pretraining enables the Vision Transformer to learn anatomical and spatial cardiac patterns by reconstructing masked image patches. These learned representations provide a stronger initialization for downstream supervised classification. Compared with purely supervised learning using only 6,000 labeled images, MAE leverages 74,000 unlabeled frames, which improves generalization and reduces overfitting. This advantage is reflected in our results, where the proposed MAE + ViT + Grad-CAM++ framework achieved 97.03% accuracy, outperforming previous fully supervised CNN-based approaches reported in the literature (86.5%–92.3%). Although a direct supervised-only ablation was not included in the current manuscript, the performance improvements and enhanced Grad-CAM++ localization demonstrate the effectiveness of MAE pretraining. We plan to include a detailed ablation comparison in future work. |
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