基底细胞癌组织病理学信息提取系统数据表3

数据集获取（Figshare）

• 源站标题：Datasheet3_Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing.docx
• DOI：10.3389/fsurg.2022.870494.s003
• 发布日期：2022-11-09
• 文件数：1 个
• 文件总体积：351.56 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Datasheet3_Development_and_validation_of_an_automated_basal_cell_carcinoma_histopathology_information_extraction_system_using_natural_language_processing_docx/21525735

命令行下载

curl -L -o "Datasheet3_Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language process.docx" "https://ndownloader.figshare.com/files/38156982"

文件清单

• Datasheet3_Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language process.docx（351.56 KB）

文件总体积 351.56 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

IntroductionRoutinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data.
MethodsWe used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care.
ResultsWe identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1–96.9), 84.2% (95% CI: 72.8–96.1), and 84.5% (95% CI: 73.0–95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen’'s Kappa score on annotated tokens was 0.85.
ConclusionUsing an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fsurg.2022.870494.s003（source: https://frontiersin.figshare.com/articles/dataset/Datasheet3_Development_and_validation_of_an_automated_basal_cell_carcinoma_histopathology_information_extraction_system_using_natural_language_processing_docx/21525735）。