CT扫描中心脏骤停后缺氧缺血性脑病检测深度学习数据集（2D与3D方法对比）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.PDF
• DOI：10.3389/fnins.2024.1245791.s001
• 发布日期：2024-02-14
• 文件数：1 个
• 文件总体积：204.57 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning-enabled_detection_of_hypoxic_ischemic_encephalopathy_after_cardiac_arrest_in_CT_scans_a_comparative_study_of_2D_and_3D_approaches_PDF/25216370

命令行下载

curl -L -o "Data_Sheet_1_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D app.PDF" "https://ndownloader.figshare.com/files/44539157"

文件清单

• Data_Sheet_1_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D app.PDF（204.57 KB）

文件总体积 204.57 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

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.
Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).
ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.
ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2024.1245791.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning-enabled_detection_of_hypoxic_ischemic_encephalopathy_after_cardiac_arrest_in_CT_scans_a_comparative_study_of_2D_and_3D_approaches_PDF/25216370）。