构建可公开使用的皮肤病病理图像数据库，助力教学与智能分析。
Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow
- 结合深度学习与标题分析的混合方法自动分类图像
- 获7772张标注图像，混合方法准确率达90.4%
- 适合医学教育、算法评测及研究者使用
临床医生和皮肤病病理培训人员常面临获取高质量、开放获取的皮肤病病理图像数据集的困难。为建立一个全面的开放数据集，用于教学、交叉参考和机器学习，我们采用混合工作流程从文献仓库PMC中整理并分类图像。通过特定关键词提取相关图像，并利用新型混合方法结合深度学习图像模态分类与图注分析进行分类。在651张人工标注图像上的验证显示，深度学习方法的F-score为89.6%，关键词检索方法为61.0%，混合方法达90.4%。共获取超过7772张图像，涵盖166种诊断，且全部由资深皮肤病理学家审校。以该数据集作为挑战任务，发现当前OpenAI图像分析算法无法有效解析皮肤病病理图像。最终，我们建立了大型、同行评审、开放获取的皮肤病病理图像数据集DermpathNet，具备半自动化标注流程。
原文摘要 · Abstract (English)
Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermatopathology trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow.
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