我们的网络架构

数据集获取（Figshare）

• 源站标题：Our network architecture.
• DOI：10.1371/journal.pdig.0001037.t006
• 发布日期：2025-10-08
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Our_network_architecture_/30310213

命令行下载

curl -L -o "Table 6.xls" "https://ndownloader.figshare.com/files/58576639"

文件清单

• Table 6.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Ventricular arrhythmia (VA) is a leading cause of sudden cardiac death. Detecting VA from electrocardiograms (ECGs) using deep learning techniques has potential to improve clinical outcomes. However, developing robust deep learning models for ECG analysis remains challenging due to: (1) inter-subject diversity among different individuals, and (2) intra-subject diversity within the same subject across different physiological state over time. In this study, we address these challenges by introducing enhancements in both the pre-training and fine-tuning stages. In the pre-training stage, we propose a novel approach combining model-agnostic meta-learning (MAML) with curriculum learning (CL) to effectively address inter-subject diversity. MAML efficiently transfer knowledge from large-scale datasets and enables rapid model adaptation to new individuals using limited records. Integrating CL further enhances the effectiveness of MAML by sequentially training models from simpler to more complex tasks. For the fine-tuning stage, we propose an improved pre-fine-tuning strategy specifically designed to manage the intra-subject diversity. We evaluate our methods on three publicly available ECG datasets and one real-world clinical ECG dataset collected using a portable device. Our proposed method achieves ROC-AUC = 0.984 / F1 = 0.940 with only 10 beats per class per subject on the test set and also achieves ROC-AUC = 0.965 / F1 = 0.937 on a real-world clinical collected data. Experimental results demonstrate that our proposed approach outperforms existing comparative methods across all evaluation metrics, and have a tendency to address intra-subject diversity. Ablation studies confirm that the combination of MAML and CL leads to more uniform performance across individuals, and our enhanced pre-fine-tuning technique substantially improves model adaptation to individual-specific data.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pdig.0001037.t006（source: https://plos.figshare.com/articles/dataset/Our_network_architecture_/30310213）。